DEVELOPMENT OF A RESISTANCE BASED BIOSENSOR UTILIZING CONDUCTING
MICROFIBERS FOR MICROBIAL PATHOGEN DETECTION

By
Shannon Katie McGraw

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Biosystems Engineering – Doctor of Philosophy
2013

ABSTRACT

DEVELOPMENT OF A RESISTANCE BASED BIOSENSOR UTILIZING CONDUCTING
MICROFIBERS FOR MICROBIAL PATHOGEN DETECTION

By

Shannon Katie McGraw

Escherichia coli O157:H7 (E. coli O157:H7) is one of the U.S. military’s top pathogens of
interest for the development of rapid diagnostic systems. The enteric pathogen can cause severe
gastroenteritis and is spread through the consumption of contaminated food and water. This is of
concern to the U.S. military and warfighter because an outbreak of diarrheal disease in the field
has the ability to rapidly render a large number of warfighters ineffective in performing their
duties. Current field “portable” detection technologies can be cumbersome and require generous
quantities of chemicals to operate. In addition, the current FDA gold standard for identification
of this pathogen from food matrices takes up to 3 days to generate a confirmed positive result.
The objective of this dissertation research was to develop a rapid, novel electrochemical
biosensor based on the use of polypropylene microfiber membranes coated with a conductive
polypyrrole and antibody functionalized for the biological capture and detection of E. coli
O157:H7. In this dissertation research, an electrotextile composed of conductive polymer coated
microfibers containing functional attachment sites for biorecognition elements was developed.
The electrotextiles were optically and electrically assessed based on the polymerization
chemicals and reaction time to determine how these factors affected the resistance of the fibers.
Based on these experiments, a mathematical model was developed, optimized, and validated.

Various methods of antibody immobilization and surface blocking on the fibers were also
assessed. Using glutaraldehyde, pathogen specific antibodies were covalently attached to the
conductive microfiber electrotextiles which were then blocked using a 5% bovine serum albumin
solution. The functionalized membranes were exposed to E. coli O157:H7 cells, washed in
Butterfield’s phosphate buffer and added to a phosphate buffer electrolyte solution. When a
voltage was applied to the system, the presence of the captured pathogen on the fiber surface
resulted in an increase in resistance at the electrotextile electrode surface, indicating a positive
result. It was found that the conductivity of the components of the system, other than the
electrotextile fibers, was not statistically significant. Proof-of-concept experiments were
conducted and it was determined that the electrotextile electrode was able to differentiate
between positive and negative samples using the pathogen E. coli O157:H7 cells as the target
0

9

over a concentration range of 10 – 10 colony forming units per milliliter (CFU/mL). The
reproducibility of the sensor results was tested and it was found that the trends in the biosensor
results were reproducible. By testing the significance of the biosensor response it was
determined that the biosensor can successfully function as a yes / no screening system. The
results show that the biosensor has an experimental lower limit of detection of 3.23 x 10
CFU/mL for the detection of E. coli O157:H7 in pure culture.

0

Copyright by
SHANNON KATIE MCGRAW
2013

ACKNOWLEDGEMENTS

I would first like to sincerely thank everyone who has contributed, assisted, and supported this
research over the years. I would like to especially thank Dr. Evangelyn Alocilja for believing in
me and helping make all of this possible. She has been a friend and mentor to me since my
freshman year of college and has molded me into the scientist I am today. Her support has been
absolutely crucial to the success of my graduate career and shaping me into a responsible
scientist. I would like to thank the SMART scholarship program and in particular my SMART
sponsor, Dr. Andy Senecal, who has always kept me focused on the big picture. I would also
like to thank Kris Senecal and the rest of my lab mates at Natick labs for their support and
assistance in helping me learn to use my research in an applied setting to help people. I would
like to thank the members of my Ph.D. guidance committee: Dr. John Gerlach, Dr. Wei Liao, and
Dr. Joan Rose for their support and guidance. As stressful as this experience has been at times, I
know they have always been working to make me better. I would also like to take this
opportunity to recognize the three people who assisted me on a day to day basis with my work:
Gianna Prata, Krista Lueck, and Patrick Fewins.

I would also like to extend my sincerest gratitude to my friends and family for all their support,
patience, and belief in me during my Ph.D. pursuit, in particular my mother, my father, my
brother Mike, and my two closest friends through this experience, Mike Anderson and Michelle
Packard. Without all of you I would have never made it this far. Everything I have
accomplished has been as a result of your love and support.
Shannon McGraw

v

TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................................... x
LIST OF FIGURES ..................................................................................................................... xiv
Chapter 1 : Introduction .................................................................................................................. 1
1.1 Hypothesis ............................................................................................................................ 3
1.2 Research objectives............................................................................................................... 3
1.3 Research significance and novelty ........................................................................................ 4
Chapter 2 : Literature review .......................................................................................................... 6
2.1 Escherichia coli O157 in the U.S. and military .................................................................... 6
2.1.1 Traditional methods of detection ................................................................................... 9
2.1.1.1 Colony counting ....................................................................................................... 9
2.1.1.2 Immunology based methods .................................................................................. 10
2.1.1.3 Polymerase chain reaction ..................................................................................... 12
2.1.1.4 FDA Bacteriological Analytical Manual method ................................................. 14
2.2 Biosensors ........................................................................................................................... 16
2.2.1 Optical biosensors ........................................................................................................ 17
2.2.1.1 Fluorescence .......................................................................................................... 18
2.2.1.2 Surface plasmon resonance .................................................................................... 18
2.2.1.3 Piezoelectric ........................................................................................................... 19
2.2.2 Electrochemical............................................................................................................ 19
2.2.2.1 Amperometric detection......................................................................................... 19
2.2.2.2 Potentiometric detection ........................................................................................ 20
2.2.2.3 Electrochemical impedance spectroscopy ............................................................. 21
2.3 Nonwoven fibers in biosensing........................................................................................... 21
2.3.1 Fiber fabrication ........................................................................................................... 23
2.3.1.1 Electrospinning ...................................................................................................... 23
2.3.1.2 Melt extrusion ........................................................................................................ 25
2.3.2 Surface treatments ........................................................................................................ 25
2.3.2.1 Drop on Demand .................................................................................................... 25
2.3.2.2 Atomic layer deposition ......................................................................................... 26
2.3.2.3 Conjugated polymers ............................................................................................. 27
2.3.3 Covalent biosurface modification ................................................................................ 29
2.3.4 Electrochemical detection using nonwoven fibers ...................................................... 30
2.3.4.1 Cyclic voltammetry................................................................................................ 31
2.3.4.2 Amperometry ......................................................................................................... 32
2.3.4.3 Resistivity / conductivity ....................................................................................... 33
2.4 Conclusions / outlook ......................................................................................................... 34
Chapter 3 : Synthesis and characterization of electrotextile fibers ............................................... 36
3.1 Introduction ......................................................................................................................... 36
vi

3.2 Materials and methods ........................................................................................................ 38
3.2.1 Materials ...................................................................................................................... 38
3.2.2 Synthesis ...................................................................................................................... 39
3.2.2.2 Dopant inclusion and solvent selection.................................................................. 40
3.2.2.3 Post-polymerization treatment ............................................................................... 40
3.2.2.4 Fiber platform selection ......................................................................................... 40
3.2.2.5 3TAA analysis ....................................................................................................... 41
3.2.3 Physicochemical characterization ................................................................................ 41
3.2.4 Biological experiments ................................................................................................ 42
3.2.4.1 Optical analysis ...................................................................................................... 42
3.2.4.2 Electrochemical analysis ........................................................................................ 43
3.3 Results and discussion ........................................................................................................ 43
3.3.1 Functional group selection ........................................................................................... 47
3.3.2 Dopant inclusion and solvent selection........................................................................ 51
3.3.3 Post-polymerization treatment ..................................................................................... 54
3.3.4 Fiber platform selection ............................................................................................... 58
3.3.5 Effects of 3TAA concentration .................................................................................... 60
3.3.5.1 SEM analysis ......................................................................................................... 60
3.3.5.2 Electrical resistivity ............................................................................................... 63
3.3.5.3 Elemental weight percent ....................................................................................... 64
3.3.5.4 Fluorescence .......................................................................................................... 65
3.3.5.5 Selecting 3TAA concentration ............................................................................... 67
3.3.6 Biorecognition element attachment ............................................................................. 68
3.3.6.1 Optical analysis ...................................................................................................... 68
3.3.6.2 Electrochemical analysis ........................................................................................ 70
3.4 Conclusions ......................................................................................................................... 72
Chapter 4 : An optimization model for the development of a conductive polymer coated
nonwoven electrotextile for use in biosensors .............................................................................. 74
4.1 Introduction ......................................................................................................................... 74
4.2 Materials and methods ........................................................................................................ 77
4.2.1 Materials ...................................................................................................................... 77
4.2.2 Experimental design..................................................................................................... 77
4.2.2.1 Electrotextile synthesis .......................................................................................... 77
4.2.2.2 Optimization of electrotextile ................................................................................ 78
4.2.3 Statistical analysis ........................................................................................................ 80
4.2.4 Analytical methods ...................................................................................................... 81
4.3 Results and discussion ........................................................................................................ 81
4.3.1. Mathematical model.................................................................................................... 81
4.3.2 Optimization ................................................................................................................ 95
4.3.3 Model Verification ....................................................................................................... 96
4.4 Conclusions ......................................................................................................................... 98
Chapter 5 : Antibody immobilization on conductive polymer coated nonwoven fibers for
biosensors .................................................................................................................................... 100
5.1 Introduction ....................................................................................................................... 100
5.2 Materials and methods ...................................................................................................... 102
vii

5.2.1 Materials .................................................................................................................... 102
5.2.2 Electrotextile synthesis .............................................................................................. 103
5.2.3 Cell culture preparation.............................................................................................. 103
5.2.4 Antibody immobilization ........................................................................................... 104
5.2.4.1 Passive adsorption ................................................................................................ 104
5.2.4.2 Glutaraldehyde attachment .................................................................................. 105
5.2.4.3 EDC / sulfo-NHS cross-linking ........................................................................... 105
5.2.5 Blocking of the fibers................................................................................................. 105
5.2.6 Antibody immobilization analysis ............................................................................. 106
5.2.7 Blocking agent analysis ............................................................................................. 106
5.2.8 Analytical methods .................................................................................................... 107
5.3 Results and discussion ...................................................................................................... 107
5.3.1 Selecting a fluorescent indicator ................................................................................ 108
5.3.2 Selecting an attachment method and antibody concentration .................................... 109
5.3.3 Blocking agent selection ............................................................................................ 112
5.4 Conclusions ....................................................................................................................... 116
Chapter 6 : A resistance based biosensor that utilizes conductive microfibers for microbial
pathogen detection ...................................................................................................................... 117
6.1 Introduction ....................................................................................................................... 117
6.2 Materials and methods ...................................................................................................... 121
6.2.1 Materials .................................................................................................................... 121
6.2.2 Electrotextile synthesis .............................................................................................. 122
6.2.3 Antibody Immobilization ........................................................................................... 122
6.2.4 Cell culture preparation and enumeration .................................................................. 123
6.2.5 Resistance measurements and electrical theory ......................................................... 124
6.2.5.1 Screening for ohmic behavior .............................................................................. 126
6.2.5.2 Circuit with two resistors in parallel .................................................................... 126
6.2.6 Determining system resistances ................................................................................. 128
6.2.7 Determining the system’s component contributions ................................................. 128
6.2.8 Using the electrotextile as a resistance based sensor ................................................. 129
6.3 Results and discussion ...................................................................................................... 130
6.3.1 Screening for ohmic behavior .................................................................................... 130
6.3.2 Determining system resistances ................................................................................. 131
6.3.2 Determining the system’s component contributions ................................................. 132
6.3.3 Using the electrotextile as a resistance based sensor ................................................. 134
6.3.4 Sensor reproducibility ................................................................................................ 137
6.4 Conclusions ....................................................................................................................... 143
Chapter 7 : Discussion and conclusions...................................................................................... 145
Chapter 8 : Future work ............................................................................................................. 153
APPENDICES ............................................................................................................................ 158
APPENDIX A: STANDARD OPERATING PROCEDURES .................................................. 159
A.1 FIBER SYNTHESIS PROTOCOL.................................................................................. 159
viii

A.2 OPTIMIZED FIBER SYNTHESIS PROTOCOL ........................................................... 161
A.3 ANTIBODY PASSIVE ADSORPTION PROTOCOL ................................................... 163
A.4 ANTIBODY GLUTARALDEHYDE PROTOCOL (96 WELL PLATE) ...................... 165
A.5 ANTIBODY GLUTARALDEHYDE PROTOCOL (2 CM X 2 CM SQUARE)............ 167
A.6 ANTIBODY EDC / SULFO-NHS PROTOCOL (96 WELL PLATE) ........................... 169
A.7 PICOGREEN STAINING OF CELLS ............................................................................ 171
A.8 FLUORESCENT MEASUREMENT OF CAPTURED STAINED CELLS .................. 173
A.9 ELECTROCHEMICAL MEASUREMENT USING BIOSENSOR ............................... 175
A.10 BACTERIAL CULTURE TRANSFER AND GROWTH ............................................ 177
APPENDIX B: DATA ................................................................................................................ 179
B.1 CHAPTER 3 DATA ........................................................................................................ 179
B.2 CHAPTER 4 DATA ........................................................................................................ 208
B.3 CHAPTER 5 DATA ........................................................................................................ 217
B.4 CHAPTER 6 DATA ........................................................................................................ 233
REFERENCES ........................................................................................................................... 297

ix

LIST OF TABLES

Table 1-1. Research contribution of this dissertation project to the literature ............................... 5
Table 2-1. Top 5 pathogens contributing to domestically acquired foodborne illnesses resulting
in hospitalization per year, taken from the CDC 2011 Estimates: Findings. .................................. 7
Table 3-1. Summary of treatments and effects. All reaction times are 30 minutes except for the
samples from Figure 3-1 and Figure 3-2 which had reaction times of 18 hours. ......................... 45
Table 3-2. Characterization of polypyrrole copolymer with increasing concentrations of 3TAA.
....................................................................................................................................................... 60
Table 4-1. Central composite design, from Haaland. Code values are in the parentheses, actual
values are outside of the parentheses. ........................................................................................... 79
Table 4-2. Experimental results from central composite design................................................... 82
Table 4-3. Results of regression analysis. ..................................................................................... 83
Table 4-4. Analysis of variance (ANOVA) for the regression model from 5-factor central
composite design. .......................................................................................................................... 84
Table 4-5. Optimal conditions for minimum electrotextile resistance. ........................................ 96
Table 4-6. Experimental results of optimal conditions. ................................................................ 97
Table B- 1. Data for Figure 3-7. ……………………………………………………………... 198
Table B- 2. Data for Figure 3-8. ……………………………………………………………… 199
Table B- 3. Data for Figure 3-9. ……………………………………………………………... 200
Table B- 4. Student’s T-Test of Resistivities (2 tails, α = 0.05). ……………………………... 201
Table B- 5. Data for Figure 3-12, resistances. ……………………………………………….. 202
Table B- 6. Data for Figure 3-12, responses. ………………………………………………… 204
Table B- 7. Data for Figure 3-12, average response. ………………………………………… 206
Table B- 8. Student’s T-Test of Biotin Sensor Response (2 tails, α = 0.05). ………………… 207

x

Table B- 9. Data for Table 4-2 and Table 4-3. ……………………………………………….. 208
Table B- 10. T-Test for model verification. ………………………………………………….. 216
Table B- 11. Data for Figures 5-1 and 5-2, passive adsorption. ……………………………… 227
Table B- 12. Data for Figures 5-1 and 5-2, glutaraldehyde immobilization. …………………. 228
Table B- 13. Data for Figures 5-1 and 5-2, EDC / sulfo-NHS immobilization. ……………... 229
Table B- 14. Data for Figures 5-5, and 5-6 (0 washes). ……………………………………… 230
Table B- 15. Data for Figures 5-4, 5-5, and 5-6 (1 wash). Outlier removed from No Cells, Goat
Block using Dixon’s Q-test. …………………………………………………………………... 231
Table B- 16. Data for Figures 5-5 and 5-6 (3 washes). Outliers removed from E. coli O157:H7,
BSA Block and Salmonella Enteritidis, Goat Block using Dixon’s Q-test. ………………… ..232
Table B- 17. Data for Figure 6-4. …………………………………………………………….. 233
Table B- 18. Data for Figures 6-5 and 6-6. …………………………………………………… 234
Table B- 19. Data for Figure 6-5, RT. …….............................................................................. 235
Table B- 20. Data for Figure 6-6, R2. ………………………………………………………… 236
Table B- 21. Calculated values of R1. ……………………………………………………….. 237
Table B- 22. Calculated values of ∆R. ………………………………………………………... 238
Table B- 23. Percent change due to R2. ……………………………………………………… 239
Table B- 24. Student’s T-Test comparing RT and R1 (2 tails, α = 0.05). ……………………. 240
Table B- 25. Resistances of blank sample. …………………………………………………... 241
Table B- 26. Data for Figures 6-7, 6-8, and 6-12 experiment A. …………………………….. 242
Table B- 27. Data for Figure 6-7, 6-8, and 6-12, experiment A resistance. ………………….. 243
Table B- 28. Data for Figure 6-7, experiment A. ……………………………………………... 245
Table B- 29. Data for Figure 6-8, experiment A. ……………………………………………... 246
Table B- 30. Student’s T-Test of data, experiment A. ………………………………………... 246

xi

Table B- 31. Data for Figures 6-9 and 6-12, experiment B. ………………………………….. 247
Table B- 32. Data for Figures 6-10 and 6-12, experiment C. ………………………………… 248
Table B- 33. Data for Figures 6-11 and 6-12, experiment D. ………………………………... 249
Table B- 34. Data for Figure 6-12, experiment E. ……………………………………………. 250
Table B- 35. Data for Figure 6-12, experiment F. ……………………………………………. 251
Table B- 36. Data for Figure 6-12, experiment G. ……………………………………………. 252
Table B- 37. Data for Figure 6-12, experiment H. ……………………………………………. 253
Table B- 38. Data for Figure 6-12, experiment A response. ………………………………….. 254
Table B- 39. Experiment A average response. ……………………………………………….. 256
Table B- 40. Data for Figures 6-9 and 6-12, experiment B resistance. ………………………. 257
Table B- 41. Data for Figures 6-9 and 6-12, experiment B response. ………………………... 259
Table B- 42. Data for Figure 6-9, experiment B average response. ………………………….. 261
Table B- 43. Data for Figures 6-10 and 6-12, experiment C resistance. ……………………... 262
Table B- 44. Data for Figures 6-10 and 6-12, experiment C response. ………………………. 264
Table B- 45. Data for Figure 6-10, experiment C average response. ………………………… 266
Table B- 46. Data for Figures 6-11 and 6-12, experiment D resistance. ……………………... 267
Table B- 47. Data for Figures 6-11 and 6-12, experiment D response. ………………………. 269
Table B- 48. Data for Figure 6-11, experiment D average response. ………………………… 271
Table B- 49. Student’s T-Tests of responses (2 tails, α = 0.05). ……………………………... 272
Table B- 50. Data for Figure 6-12, experiment E resistance. ………………………………… 276
Table B- 51. Data for Figure 6-12, experiment F resistance. ………………………………… 277
Table B- 52. Data for Figure 6-12, experiment G resistance. ………………………………… 279
Table B- 53. Data for Figure 6-12, experiment H resistance. ………………………………… 281

xii

Table B- 54. Data for Figure 6-12, cumulative response experiments A-H. …………………..282
Table B- 55. Data for Figure 6-12, cumulative average response. …………………………… 294
Table B- 56. Student’s T-Test cumulative responses of experiments A – H (2 tails, α = 0.05). …..
…………………………………………………………………………………………………. 295

xiii

LIST OF FIGURES

Figure 2-1. Schematic representation of a biosensor and its components. (For interpretation of
the references to color in this and all other figures, the reader is referred to the electronic version
of this dissertation.)....................................................................................................................... 16
Figure 2-2. Schematic representation of electrospinning process. ............................................... 24
Figure 3-1. SEM image of nylon 6 fibers coated in polypyrrole with 3-COOH. Scale bar equal
to 10 µm, magnification of 2,000x, EHT of 30 kV and working distance of 6.0 mm. ................. 48
Figure 3-2. SEM image of nylon 6 fibers coated in polypyrrole with 3TAA. Scale bar equal to
20 µm, magnification of 2,000x, EHT 30 kV, and working distance of 6.0 mm. ........................ 49
Figure 3-3. Schematic representation of the chemical structure of poly(pyrrole-3TAA)............. 50
Figure 3-4. SEM image of nylon 6 fibers coating in doped polypyrrole with methanol as solvent.
Scale bar equal to 20 µm, magnification of 2,000x, EHT of 30 kV and working distance of 6.0
mm. ............................................................................................................................................... 52
Figure 3-5. SEM image of nylon 6 fibers coated in doped polypyrrole with water as solvent.
Scale bar equal to 10 µm, magnification of 2,000x, EHT of 30 kV, and working distance of 8.0
mm. ............................................................................................................................................... 53
Figure 3-6. SEM image of nylon 6 fibers coated in doped polypyrrole with no rinse postpolymerization. Scale bar equal to 100 µm, magnification of 500x, EHT of 30 kV and working
distance of 6.0 mm. ....................................................................................................................... 55
Figure 3-7. SEM image of nylon 6 fibers coated in doped polypyrrole with a DI water wash
post-polymerization. Scale bar equal to 100 µm, magnification of 500x, EHT of 30 kV and
working distance of 6.0 mm.......................................................................................................... 56
Figure 3-8. SEM image of nylon 6 fibers coated in doped polypyrrole with DI water wash and
sonication post-polymerization. Scale bar equal to 100 µm, magnification of 500x, EHT of 30
kV and working distance of 6.0 mm. ............................................................................................ 57
Figure 3-9. SEM image of nylon 6 fibers coated in doped polypyrrole. Scale bar at 10 µm,
magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm. ................................. 58
Figure 3-10. SEM image of polypropylene fibers coated in doped polypyrrole. Scale bar at 10
µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm. .......................... 59

xiv

Figure 3-11. SEM image of fibers with polymer coating with a 3TAA concentration of 10
mg/mL. A smooth conformal polymer coating was observed along the individual fibers with
minimal polymer clusters. Scale bar equal to 10 µm, magnification of 5,000x, EHT of 30 kV
and working distance of 6.0 mm. .................................................................................................. 61
Figure 3-12. SEM image of fibers with polymer coating with a 3TAA concentration of 100
mg/mL. The coating was rough with a large amount of polymer built up along the surface,
engulfing several fibers and reducing the porosity of the membrane. Scale bar equal to 10 µm,
magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm. ................................. 62
Figure 3-13. Change in sample resistivity based on increasing 3TAA concentrations, with error
bars representing the standard deviation of each sample. The overall trend showed resistivity
increasing as concentration of 3TAA increases, starting at 1 mg/mL. ......................................... 63
Figure 3-14. Change in sample resistivity based on increasing 3TAA concentrations excluding
100 mg/mL 3TAA, with error bars representing the standard deviation of each sample. ............ 64
Figure 3-15. Change in sulfur weight percent at 100x magnification using EDS based on
increasing 3TAA concentrations. The overall trends shows the sulfur weight percentage
increasing as the concentration of 3TAA increases in the sample, starting at 1 mg/mL. ............. 65
Figure 3-16. Change in average fluorescent output after FITC-avidin binding based on
increasing 3TAA concentrations with error bars showing the standard deviation for each sample.
The overall trend shows the average fluorescence output increasing as the concentration of
3TAA increases in the sample, starting at 0.1 mg/mL. ................................................................. 66
Figure 3-17. False colored confocal images of single fibers with FITC-avidin attachment. A:
Fiber reflectance. B: Bound FITC-avidin. C: Composite image. All at 4,000× with lasers at 405
and 488 nm. ................................................................................................................................... 69
Figure 3-18. False colored confocal images of fibers with biotinylated quantum dot attachment.
A: Fiber reflectance. B: Bound biotinylated quantum dots. C: Composite image. All at 4,000×
with lasers at 405 nm. ................................................................................................................... 70
Figure 3-19. Average response over a period of 12 minutes (after a 3 minute equilibrium time)
for the electrochemical detection with varying biotin solution concentrations. ........................... 71
Figure 4-1. The effects of reaction conditions on electrotextile resistance at 55% pyrrole, 0.025
g/mL 3TAA, and 0.1 M FeCl3. ..................................................................................................... 85
Figure 4-2. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.025
g/mL 3TAA, and 0.25 M 5SSA. ................................................................................................... 86
Figure 4-3. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.025
g/mL 3TAA, and 75 minutes. ....................................................................................................... 87

xv

Figure 4-4. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.1 M
FeCl3, and 0.25 M 5SSA. .............................................................................................................. 88
Figure 4-5. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.1 M
FeCl3, and 75 minutes. .................................................................................................................. 89
Figure 4-6. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.25 M
5SSA, and 75 minutes. .................................................................................................................. 90
Figure 4-7. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL 3TAA,
0.1 M FeCl3, and 0.25 M 5SSA. ................................................................................................... 91
Figure 4-8. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL 3TAA,
0.1 M FeCl3, and 75 minutes. ....................................................................................................... 92
Figure 4-9. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL 3TAA,
0.25 M 5SSA, and 75 minutes. ..................................................................................................... 93
Figure 4-10. The effect of reaction conditions on electrotextile resistance at 0.1 M FeCl3, 0.25 M
5SSA, and 75 minutes. .................................................................................................................. 94
Figure 4-11. SEM image of fibers made using optimal conditions for polymer coating synthesis.
Scale bar equal to 100 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0
mm. ............................................................................................................................................... 98
Figure 5-1. Average fluorescence due to attachment-reporter combinations when blocked at
varying antibody concentrations. Where ‘A’ stands for passive adsorption, ‘G’ for
glutaraldehyde attachment, ‘E’ for EDC attachment, ‘F’ for FITC labeling, and ‘P’ for PicoGreen
stain. Error bars show the range of the signal for each data set. ................................................ 108
Figure 5-2. Average fluorescence for attachment methods at varying antibody concentrations
with blocking and PicoGreen reporter. Where ‘A’ stands for passive adsorption, ‘G’ for
glutaraldehyde attachment, and ‘E’ for EDC attachment. Error bars show the range of signal for
each data set. ............................................................................................................................... 110
Figure 5-3. Fluorescent images of fibers at 200x with each attachment method, blocked, at an
antibody concentration of 100 µg/mL, with PicoGreen reporter. A: passive adsorption. B:
glutaraldehyde attachment. C: EDC / sulfo-NHS attachment. ................................................. 111
Figure 5-4. Average fluorescence for electrotextile fibers with 10 µg/mL of antibody attached
using glutaraldehyde and varying blocking methods with either no cells added, PicoGreen
stained E. coli O157:H7 added, or PicoGreen stained S. Enteritidis cells added and 1 wash
performed. Error bars show the range of signal of each data set. .............................................. 112
Figure 5-5. Average fluorescence for electrotextile fibers functionalized with anti-E. coli
O157:H7 antibodies with different blocking treatments over multiple washes with PicoGreen
xvi

stained E. coli O157:H7 cells as the fluorescent reporter. Error bars shows the range of signal of
each data set. ............................................................................................................................... 113
Figure 5-6. Average fluorescence for electrotextile fibers functionalized with anti-E. coli
O157:H7 antibodies with different blocking treatments over multiple washes with PicoGreen
stained S. Enteritidis cells as the fluorescent reporter. Error bars show the range of signal of each
data set. ....................................................................................................................................... 115
Figure 6-1. Schematic of the system design. The electrotextile fibers are connected to the
computer / potentiostat for resistance measurements. ................................................................ 124
Figure 6-2. Experimental set-up that measures the change in resistance of the antibody bound
electrotextile electrode when bacteria are added to the buffer solution. Alligator clips connect
the conductive polymer electrotextile to the computer / potentiostat where the measured current
of the system is recorded............................................................................................................. 125
Figure 6-3. Circuit model of the antibody immobilized fibers with bacteria present. The system
acts as a circuit with two resistors in parallel. R1 is the resistance at the electrotextile surface. R2
is the resistance in the buffer solution......................................................................................... 127
Figure 6-4. The average linear sweep of the electrotextile fibers over the potential range of
interest, -1.0 to 1.0 V. The vertical axis is measured current output in micro amps. The solid
line shows the linear best fit of the data points with an equation of y=2756.4x-112.49 and has an
R2 value of 0.9949....................................................................................................................... 131
Figure 6-5. Total system resistance (RT) over 15 minutes at varied bacterial cell concentrations.
As the concentration of bacterial cells increases, so does the measured system resistances. ..... 132
Figure 6-6. Resistance of the buffer solution (R2) without the electrotextile present over 15
minutes at varied bacterial cell concentrations. .......................................................................... 133
Figure 6-7. Average resistances over a period of 12 minutes (after a 3 minute equilibrium time)
for the triplicate runs of the electrotextile biosensor with varying bacterial cell concentrations,
fibers from experiment A. The resistances increase as the concentration of cells increases. A
visible difference in response can be observed between all of the samples and the control. ..... 135
Figure 6-8. Average resistances for varying bacterial cell concentrations, fibers from experiment
A. Resistances taken after 3 minutes equilibrium time for each cell concentration were averaged
to provide a single resistance value. Error bars show the standard deviations of the experimental
data. ............................................................................................................................................. 136
Figure 6-9. Average response over a period of 12 minutes (after a 3 minute equilibrium time) for
the electrotextile biosensor with varying bacterial cell concentrations, fibers from experiment B.
..................................................................................................................................................... 138

xvii

Figure 6-10. Average response over a period of 12 minutes (after a 3 minute equilibrium time)
for the electrotextile biosensor with varying bacterial cell concentrations, fibers from experiment
C. ................................................................................................................................................. 139
Figure 6-11. Average response over a period of 12 minutes (after a 3 minute equilibrium time)
for the electrotextile biosensor with varying bacterial cell concentrations, fibers from experiment
D. ................................................................................................................................................. 140
Figure 6-12. Cumulative average response at each time point of pooled multiple day data over a
period of 12 minutes (after a 3 minute equilibrium time) for the electrotextile biosensor with
varying bacterial cell concentrations. ......................................................................................... 142
Figure B- 1. SEM image of Nylon 6fibers with 3-COOH and pyrrole polymer coating,
acetonitrile solvent, 18 hour reaction time. Scale bar equal to 100 µm, magnification at 500x,
EHT of 30 kV and working distance of 6.0 mm……………………..………………………... 179
Figure B- 2. SEM image of Nylon -6 fibers with 3-COOH and pyrrole polymer coating,
acetonitrile solvent, 18 hour reaction time. Scale bar equal to 2 µm, magnification of 30,000x,
EHT of 30 kV and working distance of 6.0 mm……………………….. …………………….. 180
Figure B- 3. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, acetonitrile
solvent, 18 hour reaction time. Scale bar equal to 100 µm, magnification of 150x, EHT of 30 kV
and working distance of 6.0 mm………………………...…………………………………….. 181
Figure B- 4. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, acetonitrile
solvent, 18 hour reaction time. Scale bar equal to 2 µm, magnification of 20,000x, EHT of 30
kV and working distance of 6.0 mm………………………..…………………………………. 182
Figure B- 5. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, methanol
solvent, 5SSA dopant, 30 minute reaction time. Scale bar equal to 100 µm, magnification of
200x, EHT of 30 kV and working distance of 6.0 mm…………………... …………………... 183
Figure B- 6. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, water
solvent, 5SSA dopant, 30 minute reaction time. Scale bar equal to 20 µm, magnification of
500x, EHT of 30 kV and working distance of 8.0 mm………………………………………... 184
Figure B- 7. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, water
solvent, 5SSA dopant, 30 minute reaction time. No rinse. Scale bar equal to 2 µm,
magnification of 10,000x, EHT of 30 kV and working distance of 6.0 mm………………….. 185
Figure B- 8. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, water
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 10 µm,
magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm………………….... 186
Figure B- 9. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, water
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse and sonication. Scale bar equal
xviii

to 2 µm, magnification of 10,000x, EHT of 30 kV and working distance of
6.0mm……….………………………………………………………………………………… 187
Figure B- 10. SEM image of Nylon-6 fibers with 3TAA and pyrrole polymer coating, water
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 100 µm,
magnification of 500x, EHT of 30 kV and working distance of 6.0 mm………………… ….. 188
Figure B- 11. SEM image of polypropylene fibers with 3TAA and pyrrole polymer coating,
water solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 100
µm, magnification of 500x, EHT of 30 kV and working distance of 6.0 mm………………….189
Figure B- 12. SEM image of fibers with polymer coating containing 0 mg/mL 3TAA. Scale bar
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm….. 190
Figure B- 13. SEM image of fibers with polymer coating containing 0 mg/mL 3TAA. Scale bar
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm…... 191
Figure B- 14. SEM image of fibers with polymer coating containing 0.1 mg/mL 3TAA. Scale bar
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm….. 192
Figure B- 15. SEM image of fibers with polymer coating containing 0.1 mg/mL 3TAA. Scale
bar equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0
mm…………………………………………………………………………………………….. 193
Figure B- 16. SEM image of fibers with polymer coating containing 1 mg/mL 3TAA. Scale bar
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0
mm.……………………………………………………………………………………….…… 194
Figure B- 17. SEM image of fibers with polymer coating containing 1 mg/mL 3TAA. Scale bar
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0
mm…………..………………………………………………………………………………… 195
Figure B- 18. SEM image of fibers with polymer coating containing 10 mg/mL 3TAA. Scale bar
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0
mm…………..………………………………………………………………………………… 196
Figure B- 19. SEM image of fibers with polymer coating containing 100 mg/mL 3TAA. Scale
bar equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0
mm…………..………………………………………………………………………………… 197
Figure B- 20. Western blot 1, where 1: ladder; 2: negative for E.coli O157:H19GT164; 3:
negative for E.coli O157:H7GT126; 4: negative for E.coli O157:H7GT125; 5: positive for E.coli
O157:H7 Sakai strain; 6: negative for E.coli O157:H7 AEEC strain; 7: negative for E.coli
O26:H11; 8: negative for E.coli O55:H7; 9: positive for E.coli O157:H7 Spinach pGFPuv; and
10: negative for Shigella boydii………..……………………………………………………… 217

xix

Figure B- 21. Western blot 2, where 1: ladder; 2: negative for E.coli O157:H38 Roe 1A164; 3:
positive for E.coli O157:H45 166; 4: negative for E.coli Mastitis 1368; 5: negative for Bacillus
cereus; 6: negative for Bacillus anthracis Sterne Strain; 7: positive for Citrobacter freundii
GT4885; 8: negative for Bacillus thuringiensis; 9: negative for Bedmark generic E.coli K12; 10:
negative for Enterobacter agglomerans GT1611……...………………………………………. 218
Figure B- 22. Western blot 3, where 1: ladder; 2: positive for SNP 17 E.coli O157:H7; 3:
positive for SNP 18 E.coli O157:H7, 4: positive for SNP 19 E.coli O157:H7, 5: positive for SNP
20 E.coli O157:H7, 6: positive for EHEC1 #1 E.coli O157:H7, 7: positive for EHEC1 #2 E.coli
O157:H7; 8: positive for EHEC1 #3 E.coli O157:H7; 9: positive for EHEC1 #4 E.coli O157:H7,
and 10: positive for EHEC1 #5 E.coli O157:H7….…………………………………………… 219
Figure B- 23. Western blot 4, where 1: ladder; 2: negative for Pseudomonas aeruginosa; 3:
positive for Escherichia hermannii; 4: negative for Staphylococcus aureus 12600; 5: negative for
Staphylococcus aureus Ent AT #4; 6: positive for Enterococcus fecalis ATCC 19433; 7: negative
for Staphylococcus aureus ATCC 25923; 8: positive for Citrobacter freundii ATCC 8090; 9:
positive for EHEC1 #8 E.coli O157:H7; 10: positive for E.coli O157:H7 Spinach TW
14359………………………..…………………………………………………………………. 220
Figure B- 24. Western blot 5, where 1: ladder; 2: positive for EHEC1 #6 E.coli O157:NM; 3:
positive for EHEC1 #7 E.coli O157:NM; 4: positive for E.coli O157:H7 Ao317; 5: negative for
Shigella flexneri; 6: negative for E.coli O26:H11 BSL 326; 7: positive for Citrobacter freundii
ATCC 8090; 8: negative for Salmonella enteritis Typhimurium 0648 10/12; 9: negative for
Klebsiella pneumoniae 6-21; 10: negative for Klebsiella pneumoniae ATCC
13883………………………..…………………………………………………………………. 221
Figure B- 25. Western blot 6, where 1: ladder; 2: positive for E.coli O157:H43 GT 4316; 3:
negative for E.coli O157:H19 GT164; 4: negative for Enterobacter agglomerans GT1611; 5:
positive for E.coli O157:H7 GT632; 6: negative for E.coli O157:H7 GT126; 7: negative for
E.coli O157:H7 GT125; 8: positive for E.coli O157:NM GT4141; 9: positive for E.coli O157:H7
A110; 10: positive for Citrobacter freundii (CF3) GT5142.…………..………………………. 222
Figure B- 26. Western blot 7, where 1: ladder; 2: positive for SNP2 E.coli O157:H7; 3: positive
for SNP3 E.coli O157:H7; 4: positive for SNP4 E.coli O157:H7; 5: positive for SNP5 E.coli
O157:H7; 6: positive for SNP6 E.coli O157:H7; 7: positive for SNP7 E.coli O157:H7; 8:
positive for SNP8 E.coli O157:H7; 9: positive for SNP9 E.coli O157:H7; 10: positive for SNP10
E.coli O157:H7……………..…………………………………………………………………. 223
Figure B- 27. Western blot 8, where 1: ladder; 2: positive for SNP11 E.coli O157:H7; 3: positive
for SNP12 E.coli O157:H7; 4: positive for SNP13 E.coli O157:H7; 5: positive for SNP14 E.coli
O157:H7; 6: positive for SNP15 E.coli O157:H7; 7: positive for SNP16 E.coli O157:H7; 8:
negative for 4-22-10 (BSL #1); 9: positive for BSL #2 Bio Systems; 10: positive for E. coli
ATCC 43895(107)………….…………………………………………………………………. 224
Figure B- 28. Western blot 9, where 1: ladder, 2: positive for Citrobacter freundii CF3GT5742;
3: negative for Enterobacter agglomerans GT1611; 4: positive for E.coli O157:H43 GT 4136; 5:
xx

negative for E.coli O157:H19 164; 6: positive for E.coli O157:H7 GT632; 7: negative for E.coli
O157:H7 GT127; 8: positive for E.coli O157:H7 A110; 9: negative for E.coli O157:H7 125; 10:
positive for E.coli O157:NM GT4141...………………………………………………………. 225
Figure B- 29. Western blot 10, where 1: ladder; 2: positive for E.coli O157:H16 GT4137; 3:
positive for E.coli O157:H38 GT4138; 4: negative for E. aerogenes GT47; 5: negative for E.
cloacae GT50; 6: negative for C. freundii GT9173; 7: positive for E.coli O157:H7 GT4135; 8:
positive for E.coli O157:H7 GT4132; 9: positive for E.coli O157:H45; 10: negative for C.
freundii GT 4885……………..………………………………………………………………... 226

xxi

Chapter 1 : Introduction

Although food and waterborne pathogens do not have as significant an effect on U.S. military
operations as they have in the past, enteric pathogens are the number one cause of non-combat
related injuries in the field and are therefore one of the primary military medical concerns for
deployed troops [1]. Gastroenteritis was the leading cause of illness among troops during
Operations Desert Shield (1990 – 1991) and Desert Storm (1991) [2]. This is a significant issue
for the military to address because an outbreak of diarrheal disease in the field has the ability to
rapidly render a large number of warfighters ineffective in performing their duties. During
Operation Restore Hope (1992 - 1993) it was shown that 16% of all hospital admissions were for
diarrheal illness [3]. Of these admissions, 16% could be traced back to Escherichia coli (E. coli).
Various studies on diarrheal illness in deployed troops have listed an array of enteric source
pathogens with the most commonly occurring being: Shigella, E. coli, Salmonella, and
Campylobacter species [1-4]. Based on this information, the pathogen of interest selected for
this study is Escherichia coli O157:H7.

It is important to have rapid, inexpensive, highly sensitive detection technologies because of the
low infectious dose, potential severity of illness, and ease of E. coli O157:H7 infection spreading
to large populations. These technologies need to be environmentally robust and capable of
detecting multiple pathogens. The standard method of detection for E. coli O157:H7 does not
meet this standard. The U.S. Food and Drug Administration (FDA) standard screening method
for E. coli O157:H7 requires the blending or stomaching of food samples followed by overnight
enrichment before real-time polymerase chain reaction (PCR) screening can be done [5, 6]. Any
1

samples that are determined to be presumptive positive based on the real-time PCR screening
must then be culture confirmed. Obtaining a confirmed positive sample takes a minimum of 3
days and requires an enrichment step before isolation can be performed. In addition, in order to
perform a serological characterization multiple staining and agglutination tests must be
performed. These methods have several drawbacks. It can be difficult to recover cells from food
matrices and cells that are recovered are often not concentrated enough to provide rapid
identification [5, 6]. In addition, it is difficult to discern between colonies of different strains of
bacterium on culture plates. These methods are not easily implemented in the field. Even the
most “portable” versions still require large instrumentation, generous quantities of chemicals,
and a low risk of exposure to other contaminants [5, 6]. Because of the unreliability and long
wait time for detection using current methods the development and use of a rapid, antibodybased, pathogen specific biosensor is a priority. An improved detection method would decrease
the amount of time between contamination and warning, reduce the size of outbreaks, and help
ensure overall public safety [7].

This dissertation describes the generation of an electrochemical based biosensor for E. coli
O157:H7 detection using antibody-functionalized electrotextile electrodes. Chapter 2 presents a
review of the literature that pertains to the technologies relevant to this project including current
methods for the detection of E. coli O157:H7, nonwoven fiber fabrication, and the use of
nonwoven fibers in electrochemical detection. Chapter 3 describes the synthesis and
characterization of conductive polymer coated nonwoven fibers to create electrotextiles. Chapter
4 presents the development of an optimization model of the polymer synthesis used to develop
the electrotextile fibers. Antibody immobilization and blocking of the electrotextile fibers is

2

presented in Chapter 5. Chapter 6 describes the use of antibody functionalized pyrrole – 3thiopheneacetic acid (3TAA) copolymer coated nonwoven fibers as electrotextile electrodes in a
working electrochemical biosensor for the detection of E. coli O157:H7. Chapter 7 is a
discussion on the conclusions made in this dissertation and Chapter 8 presents recommendations
for future research based on this project. The following sections of Chapter 1 present the
research hypothesis and objectives, as well as the research significance and novelty.

1.1 Hypothesis
The research presented in this dissertation is based on the following hypothesis that electrotextile
fibers can be developed from nonwoven microfibers and they can be immuno-functionalized for
the rapid detection of E. coli O157:H7.

1.2 Research objectives
The overall objective of this dissertation research is to develop, optimize, and evaluate a rapid,
portable, and novel electrochemical biosensor utilizing antibody-functionalized electrotextile
electrodes for the capture and detection of Escherichia coli O157:H7.
The detailed objectives of this project are:
•

To develop an electrotextile composed of conductive polymer coated microfibers.

•

To optimize the polymerization process with the development of a mathematical model.

•

To functionalize electrotextile surfaces with pathogen specific antibodies.

•

To develop an electrochemical biosensor system.

•

To evaluate the biosensor for the detection of the pathogen: Escherichia coli O157:H7.

3

1.3 Research significance and novelty
The novelty of the presented research in this dissertation relies on the use of polymer coated
fibers as an electrode in a biosensor for the electrochemical detection of bacterial pathogen in a
liquid sample. Many biosensors have been developed for the detection of E. coli using metallic
electrodes such as gold [8], platinum [9], silver [10], or carbon [11]. By producing a conductive
polymer coating on nonwoven microfibers, an electrochemical biosensor electrode can be
created that is less expensive than its planar metal counterpart [12]. The overlapping fiber layers
also have more available surface area than planar electrodes, resulting in more potential target
attachment sites. In addition, these electrotextile electrodes can be engineered to be durable,
disposable, lightweight, and require minimal attachment chemistry. These qualities make them
ideal for in field use. To our knowledge, this is the first time a functionalized conductive
copolymer coated nonwoven electrotextile has been used with immobilized antibodies as an
electrode for the successful electrical detection of bacterial cells. A summary of the presented
research is listed in Table 1-1 and a comparison with the current literature illustrates the novelty
and scientific contribution [12-17].

4

Table 1-1. Research contribution of this dissertation project to the literature

Subject
Synthesis of aqueously deposited conformal coating of
polypyrrole onto electrotextile
Characterization and analysis of the effect of 3TAA inclusion
in polymerization
Use of electrotextile in optical detection of avidin / biotin
binding
Use of electrotextile in electrochemical detection of avidin /
biotin binding
Development and optimization of a mathematical model of
electrotextile polymerization
Antibody attachment to electrotextile fibers

References
This work,
[14] [16]
published [18]
This work
[17] [15]

Construction of electrochemical cell with electrotextile
electrodes
Electrochemical detection of E. coli O157:H7 in pure culture
using electrotextile electrodes

[13] [12]

5

This work,
published [18]
This work,
published [18]
This work
This work,
published [19]
This work,
published [20]
This work,
published [20]

[13]
[13]

Chapter 2 : Literature review

2.1 Escherichia coli O157 in the U.S. and military
According to the Centers for Disease Control and Prevention (CDC) “Estimates of Foodborne
Illness in the United States”, published in 2011, 1 in 6 Americans (roughly 48 million people)
will become sick from a foodborne illness every year [21]. Of those 128,000 will require
hospitalization and 3,000 will die, with children under the age of 5 and seniors over the age of 65
being at the greatest risk to develop the severe complications that lead to hospitalizations and
death. There are 31 known pathogens that are tracked by the CDC and are believed to account
for 20% of all illnesses (9.4 million cases per year), but 44% of all hospitalizations and deaths
(55,961 and 1,351 cases per year, respectively). The remainder of illnesses are caused by
unspecified agents (microbes, chemicals, or other factors that have not yet been identified or
proven to cause illness). Of these hospitalizations caused by a known pathogen, 88% can be
traced to 1 of 5 pathogens. These can be seen in Table 2-1.

6

Table 2-1. Top 5 pathogens contributing to domestically acquired foodborne illnesses resulting
in hospitalization per year, taken from the CDC 2011 Estimates: Findings.
Estimated Number of
Percentage of All Cases
Pathogen
Hospitalizations
(%)
Salmonella,
nontyphoidal
19,336
35
Norovirus
14,663
26
Campylobacter
spp.
8,463
15
Taxoplasma
gondii
4,428
8
E. coli (STEC)
O157
2,138
4
Subtotal
88

As can be seen in Table 2-1, Escherichia coli (E. coli) O157 is responsible for 4% of all cases or
2,138 domestically acquired foodborne illnesses that require hospitalization. E. coli is a gramnegative, rod shaped bacteria that is commonly found in human intestinal flora, however E. coli
O157:H7 is a less common toxin producing strain that causes serious illness when it enters the
human gastrointestinal system [22]. E. coli O157:H7 infection causes hemorrhagic colitis with
symptoms that include severe abdominal cramping and diarrhea that while initially watery can
become severely bloody and lasts an average of 8 days. Up to 15% of infections can develop
into hemolytic uremic syndrome (HUS) resulting in renal failure, hemolytic anemia, and the
permanent loss of kidney function. Common food sources of E. coli O157:H7 include ground
beef, alfalfa sprouts, lettuce, unpasteurized fruit juices, and raw milk. While the infectious dose
of E. coli O157:H7 is not known, according to the U.S. Food and Drug Administration (FDA) it
may be as few as 10 cells [23]. According to the Foodborne Diseases Active Surveillance
Network (FoodNet) Food Safety 2011 Report Card there has been a 25% decrease in E. coli
O157 infections since 2008 with a rate of occurrence of 0.98 per 100,000 people [24, 25]. This
number is still too high though, a goal rate of 0.6 per 100,000 people has been set for the year
7

2020. In addition, it is believed that for every 1 case of E. coli O157:H7 reported, roughly 26
cases go undiagnosed.

As frequent as foodborne illnesses are within the United States, they are of even greater concern
for the U.S. military. Although combat injuries are the most significant health risk during
military deployments, infectious diseases have been the largest cause of hospitalizations and lost
time in every major US war for centuries [26], particularly those caused by enteric pathogens [1].
A study of aero-medical evacuations from Iraq in 2003 found that disease and non-battlefield
injuries were 6 times more common than combat related injuries [27]. Improvements in
sanitation and hygiene practices have improved prevention [28], however military units are still
at risk due to the standard use of community kitchens, which can lead to widespread outbreaks,
rapidly rendering a large number of warfighters ineffective in performing their duties. In fact,
gastroenteritis was found to be the leading cause of illness among troops during Operations
Desert Shield (1990 – 1991) and Desert Storm (1991) [2]. During Operation Restore Hope
(1992 - 1993) it was shown that 16% of all hospital admissions were for diarrheal illness [3]. Of
these admissions 16% could be traced back to E. coli. A study of troops stationed in Iraq in 2004
found that enterotoxigenic E. coli and enteroaggregative E. coli caused 23% and 12.5% of all
diarrhea cases over one summer [29]. In Iraq and Afghanistan 77% and 54% of deployed troops
were found to have experienced diarrhea at least once [30], with the rates of diarrhea correlating
with local food consumption [31]. Various studies on diarrheal illness in deployed troops have
listed an array of enteric source pathogens with the most commonly occurring being: Shigella,
E. coli, Salmonella, and Campylobacter species [1-4].

8

Based on the potential to cause severe illness and the prevalence in the U.S. domestic and
military populations, the pathogen of interest selected for this study was Escherichia coli
O157:H7.

2.1.1 Traditional methods of detection
There are three traditional methods most commonly used for pathogen detection: colony
counting, polymerase chain reaction (PCR), and immunological methods [32]. These methods
are often used for detection because of their high sensitivity and selectivity. These methods do
have drawbacks though, they are often complex and not rapid, sometimes taking up to several
days to achieve a confirmed positive. The FDA has issued a Bacteriological Analytical Manual
(FDA BAM) in order to standardize the methods used for the detection of multiple different
foodborne pathogens in different food matrices [5, 6]. The standard method provided for the
detection of E. coli O157:H7 from foods involves growing the sample in an enrichment broth,
plating and culturing the colonies so that they can be identified phenotypically and serologically,
and finally testing for Shiga toxin genes using PCR. This method has been updated in the most
recent eBAM to recommend performing a quick screening using real-time PCR which can be
conducted in 24 hours, however it still takes 3 days in order to get a confirmation. Detailed
descriptions of these methods can be found below.

2.1.1.1 Colony counting
Colony counting involves the use of selective growth media in order to detect different bacteria
species [32]. These media often contain either inhibitors or specific substrates in order to affect
the growth of bacteria, either by inhibiting non-target strains or by causing particular colonies to
9

appear different colors in order to differentiate them [33]. Following growth of the bacteria of
interest, the colonies are assessed optically and counted in order to determine the sample
concentration.

Colony culturing and counting is the oldest method for the detection and enumeration of bacteria
and has become the standard that every other detection method is compared against. It is
generally the standard method of detection used by the FDA because of its accuracy and
reproducibility [6]. Despite being the standard it has many drawbacks, first and foremost the fact
that it is time consuming. It can often take multiple days to get a negative result, and more to
confirm a positive one. A positive result can sometimes require additional tests and culturing to
be performed, especially if it is necessary to identify the serotype of the pathogen. Colony
counting also requires many different kinds of media, antibiotic supplements, and incubators,
making it not viable as a field based testing system.

2.1.1.2 Immunology based methods
Immunological based methods use antibodies in order to detect pathogens. Antibodies are
defined as proteins that bind to a particular target antigen [34]. For the detection of bacteria,
antibodies are often generated against the bacteria surface antigens such as O (terminal sugars),
K (capsule components), H (flagella), and LPS (cell surface lipopolysaccharide) antigens.
Antibodies are produced by plasma cells in response to infections or immunizations. The
specificity of an antibody depends on the type of antibody, the species of host used, individual
differences between hosts, and the extent of purification conducted. Antibodies can be
monoclonal, polyclonal, or recombinant [32]. Monoclonal antibodies are made in lag phase with
10

cell culture by a single clone and target the same antigen [34]. Polyclonal antibodies are
typically purified from blood serum and contain a mixture of several antibodies that target
multiple different portions of the same target antigen [34]. Recombinant antibodies are synthetic
antibodies that are created using antibody genes either made in a laboratory or taken from human
cells [35].

One of the most common immunology based tools to aid in the detection of bacteria is
immunomagnetic separation (IMS), a pre-concentration step where magnetic beads that have
been coated with antibodies to the pathogen of interest are inserted into the sample and then
removed using a magnet [36-38]. The extracted beads pull the captured target pathogen from the
sample matrix with them. IMS has been combined with nearly every type of detection method
available [32]. It can be particularly useful when the sample is complex, either due to the matrix
that the pathogen is in or because there are many different kinds of bacteria present.

Another standard immunologically based technique is the enzyme-linked immunosorbent assay
(ELISA) [34]. ELISAs benefit from the specificity of antibodies but also the sensitivity of
enzyme assays by combining the two together. In the most common version, a sandwich ELISA,
an antigen specific antibody is immobilized within a well, normally the well of a 96 well plate.
The sample containing the antigen is added and then removed, leaving behind any antigen that
has bound to the immobilized antibodies. Then a second enzyme-labeled antibody specific to the
antigen is added and removed, leaving behind only the antibodies which had bound to the bound
antigen. Finally, the enzyme’s substrate is added to the well and if the antigen is present a
change in color in the well can be observed. The color change will only occur if the antigen is

11

present because the enzyme labeled antibody will only remain to react if the antigen is present in
the well for it to bind to [32, 34].

The use of immunology based methods benefits from the specificity of the antibody-antigen
binding. Antibodies can be expensive though and detection methods like ELISA may have to be
paired with a separation step if the matrix is complex. This is especially true if the detection
signal is a visual change (e.g., color), which can be obscured by a sample matrix that is opaque
or brightly colored. In addition, antibodies are proteins and therefore qualities of the sample
such as temperature or pH have to be monitored so as to not render the antibodies ineffective.
Many antibodies available for sale have cross-reactivities, meaning that the antibody reacts and
binds to an antigen that is not the antigen that was used to produce the antibody. Antibodies for
E. coli O157:H7 have been reported to have some level of cross-reactivity with Klebsiella,
Enterobacter, Citrobacter, and non-pathogenic E. coli [39].

2.1.1.3 Polymerase chain reaction
PCR is a nucleic acid amplification technique used to detect targeted bacteria based on specific
short sequences of their genetic material [40]. It combines steps to perform DNA isolation,
amplification, and quantification based on specific target DNA probes. In PCR many cycles are
completed to exponentially amplify the sample until enough target DNA has been formed for
detection. Briefly, in one cycle targeted double stranded DNA (dsDNA) is first denatured by heat
then specific primers are added which recognize the corresponding DNA strands.
Polymerization occurs, extending the DNA strands bound to the primers until 2 new identical
dsDNA sequences are formed. These newly formed dsDNA strands are then used as the target
12

DNA in the next cycle. Gel electrophoresis is then used to detect the DNA. There are several
different variations of PCR including: real-time PCR, multiplex PCR, and reverse transcriptase
PCR. Real-time PCR is faster than regular PCR, it uses fluorescent emission with a specific dye
attached to the targeted amplicon rather than gel electrophoresis for detection [41, 42]. The
fluorescent intensity is then interpreted to be proportional to the amount of amplified product
generated. Multiplex PCR can be used to detect several targets at the same time by using
multiple different primers to amplify more than one specific gene [43, 44].

PCR can be much less time consuming than other bacterial detection techniques, however it can
still take up to 24 hours and usually requires purification and enrichment steps beforehand [32].
PCR has a high level of complexity when compared to other detection techniques and is
susceptible to environmental contamination, making it not viable for in field testing. PCR
detection does have very high specificity of detection though, basing its detection on the DNA of
the target sample. This requires the use of a primer developed to be specific to a section of the
DNA sequence of the target pathogen. The primer must be known and fabricated beforehand in
order to be able to perform detection though, and if the sequence is not specific enough, then
detection specificity will be lost [32]. One of the biggest problems with using PCR to detect E.
coli O157:H7 in food samples or complex matrices is that the most important virulence factors,
Shiga toxin 1 (stx1) and Shiga toxin 2 (stx2), are not specific to the pathogen, they can be found
in other Shiga-toxin producing E. coli as well [45]. This requires the use of multiplex PCR
assays for the Shiga-toxin genes as well as pathogen-specific sequences in order to obtain a
definitive detection. Also, components in food samples or other complex matrices (e.g., calcium,

13

fat, complex polysaccharides) can inhibit the PCR, requiring additional purification and sample
preparation techniques be used [45].

2.1.1.4 FDA Bacteriological Analytical Manual method
In the FDA BAM method for the detection of E. coli O157:H7 [6] food samples are blended or
stomached, followed by overnight enrichment in modified Buffered Peptone Water with
pyruvate (mBPWp) [46] and Acriflavin-Cefsulodin-Vancomycin (ACV) antibiotic supplements.
Using either a SmartCycler II or LightCycler ® 2.0 [47-49] platform real-time PCR is used to
test the enriched samples for stx1 and stx2 genes as well as the +93 single nucleotide
polymorphism in the uidA gene, which encodes for β-D-glucuronidase (GUD) enzyme [50]. If
the result comes back positive, then a confirmation must be conducted using cultural isolation.

In order to perform the cultural isolation Butterfield’s phosphate buffer with ACV supplements
is used for overnight enrichment. The enriched sample is then serially diluted in Butterfield’s
phosphate buffer and spread or streak plated onto Tellurite Cefixime – Sorbitol MacConkey
Agar (TC-SMAC) and a chromogenic agar such as Rainbow® Agar O157 and incubated for 18 –
24 hours. E. coli O157:H7 colonies will appear colorless or neutral / gray with a smoky center
on TC-SMAC while non-O157 sorbitol-fermenting E. coli will be pink or red. On Rainbow
Agar O157:H7 will appear black or blue – black. Portions of the isolated colonies that test
positive for O157 antigen using a latex agglutination test are then streaked onto Trypticase Soy
Agar with Yeast Extract (TSAYE) plates to check for purity. ColiComplete (CC) discs are
added to the heaviest streaks on the TSAYE plates as well as a similar plate containing a positive
control, MUG positive strain of E. coli, and incubated for 24 hours. The CC discs contain a
14

chromogenic assay for galactopyranosidase (X-gal) which will appear blue on and around the
disc indicating coliform growth. It also contains a fluorogenic assay for glucuronidase (MUG)
which will fluoresce under long wave UV (365 nm) which indicates non-O157 E. coli. E. coli
O157:H7 is X-gal (+) and MUG (-). Following this test a spot indole test is conducted by taking
the spot growth from the TSAYE plate and adding it to a filter wetted with Kovac’s reagent,
where E. coli O157 will appear indole (+).

Samples that are found to be X-gal (+), MUG (-), and indole (+) must have additional tests
performed to confirm the positive result. The colonies from the TSAYE plate are tested using
the RIM E. coli O157:H7 latex test using commercial antisera to confirm the presence of O157
and H7 antigens. If the latex test comes back O157 (+) and H7 (+) then the O157:H7 serotype is
confirmed, however if the test comes back O157 (+) and H7 (-) then more tests must be
conducted to assure that the sample is not a non-motile variant (O157:NM). Tests that are
O157:H7 (+) are tested using API20E or VITEK to identify the sample as E. coli.
Both H7 (+) and H7 (-) samples must be retested to verify their toxigenic potential using either
real-time PCR (which can also be used for initial screening) or a conventional 5P multiplex PCR.
The 5P multiplex PCR looks for stx1, stx2, +93 uidA SNP, and the enterohemolysin (ehxA) genes
and gamma (γ) intimin (eae) allele (O157:H7 virulence factors) [51].

If the food sample to be tested is complex, either due to the food matrix or the sample containing
many bacterial samples, IMS beads can be used on the sample after the overnight enrichment.
This will help isolate the pathogen of interest for PCR or plate counting.

15

2.2 Biosensors
The requirement for small, ultrasensitive, affordable, disposable sensors for medical diagnostics,
environmental monitoring, and food safety has led to an increase in research in biosensor
technology. The International Union of Pure and Applied Chemistry (IUPAC) has defined
biosensors as “a device that uses specific biochemical reactions mediated by isolated enzymes,
immunosystems, tissues, organelles or whole cells to detect chemical compounds usually by
electrical, thermal, or optical signals” [52, 53], put simply, the purpose of a biosensor is to
convert the occurrence of biological events into measurable responses [54]. A schematic
representation of a biosensor and its components can be seen in Figure 2-1.

Analyte

Bioreceptor

Signal Processing

Transducer

Antibodies
Nucleic
Acids
Aptamers
Enzymes
Whole Cells

Electrochemical
Electrical
Optical
Mechanical
Magnetic

Amplifier

Data
Acquisition

Figure 2-1. Schematic representation of a biosensor and its components. (For interpretation of
the references to color in this and all other figures, the reader is referred to the electronic version
of this dissertation.)

16

In a biosensor a bioreceptor, such as antibodies or nucleic acids, are bound or in some way
closely associated with a transducer. When a biological recognition event occurs between the
bioreceptor and its target analyte, the transducer converts this event into a measurable signal.
Biosensors can be grouped based on several different criteria, such as their target analyte,
biomolecule receptor type, or method of signal transduction. The most common method is based
on signal transducer, with optical and electrochemical being the most common [52, 55-57].
Optical biosensors are often used because they have better sensitivity and selectivity than
electrochemical systems, however that advantage can be greatly reduced if the sample is turbid
or it is difficult to perform analyte extraction. Optical biosensing systems are also generally
more expensive than electrochemical systems, making them not cost beneficial in some scenarios
[58]. In order for biosensor technology to advance and surpass common pathogen detection
techniques, such as PCR and culture / colony counting, the current drawbacks of biosensors must
be addressed. These include high cost, low durability, a lack of environmental robustness for in
field testing, detection limits that do not reach those of traditional techniques, and a necessity for
extra extraction methods to be performed before use adding to the total detection time [32, 52,
59, 60].

2.2.1 Optical biosensors
Optical sensors measure changes in the optical properties of an analyte, often using fluorescence.
It can also be expanded to include such techniques as surface plasmon resonance (SPR) and
piezoelectric systems [32]. Optical biosensors have been developed for the detection of not just
bacteria, but also toxins, drugs, and other contaminants [61-64]. Optical sensing systems are

17

considered to be beneficial due to their selectivity and sensitivity, these can be reduced though if
the sample is complex.

2.2.1.1 Fluorescence
Fluorescence is defined as luminescence that is caused by a valence electron being excited from
its ground state to an excited singlet state [65]. This excitation occurs when light of a sufficient
energy is absorbed. When the electron returns to its original ground state it emits a photon of a
lower energy. Fluorescence is also characterized as having little thermal loss in the system and
rapid light emission after excitation. Fluorescence is the method most often used for optical
biosensing [65]. It can be combined with established techniques such as ELISA or PCR or can
be a detection method on its own if fluorescent markers, such as fluorescein isothiocyanate
(FITC) are used [66]. Another form of fluorescent biosensing is fluorescence resonance energy
transfer (FRET) where the capture of the target analyte results in a decrease in distance between
donor and acceptor fluorophores associated with the bioreceptor [67]. This decrease in distance
allows the acceptor fluorophore to become excited by the donor fluorophore, emitting a
fluorescent signal.

2.2.1.2 Surface plasmon resonance
In SPR based sensors, structural alterations occur near a thin film metal surface resulting in
changes in the measured refractive index [68]. Briefly, a glass plate that has been covered with a
gold thin film is irradiated with a p-polarised light from the back using a hemispherical prism.
The reflectivity is then measured as a function of the angle of incidence, θ. The resulting plot
curve will contain a dip known as the SPR minimum. The properties of the gold-solution
18

interface will dictate the angle position of this minimum, allowing for detection. SPR has been
used in the analysis of adsorption phenomena, antigen-antibody affinity binding, and bacterial
detection [32, 69, 70].

2.2.1.3 Piezoelectric
Piezoelectric systems use a quartz crystal microbalance (QCM) which has observable changes in
resonance frequency when there are mass changes on the sensor surface [71]. These changes are
usually visualized through the use of a fluorescent label. By using immobilized antibodies on the
probes a QCM has been shown to be capable of piezoelectrically detecting bacteria, such as E.
coli K12 [72], Listeria monocytogenes [73], and Salmonella species [74].

2.2.2 Electrochemical
In an electrochemical biosensor, the biological recognition element is immobilized at an
electrode which then converts the biological recognition event (e.g., antibody – antigen binding)
into a measurable electrical signal. Electrochemical sensors are often subdivided based on the
electrical parameter that is being observed for changes at the electrode-matrix interface as either
amperometric (change in current), potentiometric (change in potential), or impedimetric (change
in impedance) [32].

2.2.2.1 Amperometric detection
Amperometry uses the stepping of the potential of the working electrode from where no faradaic
reaction occurs to a potential where the surface concentration of the electroactive species at the
19

electrode is effectively zero [75]. In this method, the current over time is monitored.
Amperometry is used to measure the diffusion coefficient of electroactive species or surface area
of the working electrode. It can also be used to study the mechanisms of the electrode processes.
Because mass transport is diffusion based, in the conditions of amperometry the current-time
curves can be used to show the change in concentration gradient in the area surrounding the
surface. The gradual increase of the size of the diffusion layer paired with the depletion of the
reactant results in a decreasing slope in the concentration profile over time. This can be seen in
the current decay that occurs over time [75].

2.2.2.2 Potentiometric detection
Potentiometric detection monitors changes in the system potential and is the least common
technique used in biosensors. This can be observed when an enzyme catalyses a reaction and
consumes or generates a substance that can be detected by the ion-selective electrode. Another
form of potentiometric detection are ion selective field effect transistors (ISFETs). When
modified with antibodies, DNA , enzymes, or whole cells they can be used to monitor
biorecognition events [76, 77]. An ISFET will create regions of excess charge in a
semiconducting substrate using an electric field in order to increase or decrease conductivity.
Potentiometric techniques, such as ISFET, are not often used for biosensing because they are
complex and biomolecule immobilization can be difficult. In addition they can also have poor
reproducibility and stability [32].

20

2.2.2.3 Electrochemical impedance spectroscopy
Electrochemical impedance spectroscopy (EIS) uses a small amplitude cyclic function with
variable frequency applied to a transducer to calculate impedance based on the resulting current.
The system impedance is determined based on the amplitude of the current and potential signals
as well as the resulting phase difference that occurs between the voltage and current [78, 79].
Because EIS is very complicated to calculate, the calculations are often simplified by viewing
the system as equivalent circuits consisting of capacitors and resistors. This has allowed
biosensors to be developed using EIS for the detection of bacterial pathogens such as E. coli
O157:H7 [80].

2.3 Nonwoven fibers in biosensing
One approach for addressing some of the drawbacks of current biosensor technology is through
the development / use of electrotextiles. Nonwoven fabrics are one material being explored for
electrotextile development due to their inherent high surface area and commercial availability
[81]. The convergence of electronics, electrical engineering, and textile technologies has the
potential to combine the attractive attributes of each technology into making fabric based
networks for electrical systems, paving the way for the development of fully integrated high
surface area smart textiles with transistors, integrated circuits, sensors and other electronic
devices built into the textile structures [82].

Nonwoven fabrics are broadly defined as sheet or web structures bonded together by entangling
fibers or filaments, forming flat, porous sheets made either directly from separate fibers or from
molten plastic or plastic film [83]. The term nonwoven implies that the fabric was produced by a
21

method other than knitting, weaving, braiding, or tufting [84]. Nonwoven fibers are of particular
interest because of their small diameters (in the micro or nano scale), increased surface area, and
their ability to maintain membrane porosity in comparison to larger micron diameter nonwoven
fibers and continuous cast films [85]. These interconnected pores can result in essentially
“soldered junctions” after the annealing process is completed, resulting in higher textile
durability and tensile strength. They are also inexpensive to produce and lightweight, ideal
qualities for in field use [86, 87]. The chemical composition of nonwoven fibers and their
coatings can also be easily changed or adjusted based on their intended use. Small changes to
the processing parameters can change the fiber diameter, mesh size, porosity, texture, or weave
pattern. This processing flexibility makes them a very versatile material for sensor development.
They can be designed to be used with many different analytes and experimental conditions and
can be designed to have high chemical stability [85].

Reviews of the literature indicate that biosensor research focusing on nonwoven fibers can be
divided into the production, characteristics, and applications of nanofibers and microfibers.
Nonwoven optical biosensing is a relatively new area of research and studies in this area are
limited [88]. The majority of electrochemical work with nonwoven fibers has been for the
development of biosensors to detect compounds, such as glucose, urea, and hydrogen peroxide
[88]. Presented in the subsequent sections are descriptions of the common functional
modification techniques used for the development of nonwoven based biosensors. Also
presented are the current optical and electrical based systems that exist for biosensing using
nonwoven fibers. The aim of the next portion of the review is to present the different biosensing
applications being developed using nonwovens.

22

2.3.1 Fiber fabrication
Typically, commercial nonwovens are produced by dry-laid, wet-laid, spunbound, meltblown,
and/or spunlaced processes [89, 90]. The production process usually consists of three primary
steps: spinning the polymer, collecting continuous filaments, and bonding the fiber mat for
greater strength and functionality, using either thermal, chemical and / or mechanical techniques
[91]. Polymer fibers have typically been produced by solution / gel spinning, melt spinning, or
electrospinning [81, 92]. There are a number of excellent review articles on electrospinning and
the effects of process variables for producing polymer fibers [81, 88, 93-98]. Fiber diameters
produced by these methods can range from nano to micrometer in scale, with micron diameters
more reproducible. The selected method used to synthesize nonwovens will ensure specific
characteristics in the material [91]. This allows for the design of nonwoven fibers with an array
of characteristics, making them versatile in fabric production for commercial products.

2.3.1.1 Electrospinning
Electrospinning is a well-established process that uses electrostatic forces to produce nonwoven
nanofibers [99], a schematic of which can be seen in Figure 2-2.

23

Syringe with Polymer
Solution

High Voltage Supply

Fibers
(+)

(-)
Ground

Figure 2-2. Schematic representation of electrospinning process.

Briefly, during electrospinning, the polymer is either in solution or a melt. A voltage is applied to
the polymer solution / melt, producing electrically charged jets that are collected by an
oppositely charged collector. As the charged jet travels to the grounded collector, the solvent /
melt evaporates and cools producing nanofibers on the collector. A large number of polymers
can be spun, with the only limitation being the polymer’s solubility or ability to be used as a
melt. Additionally, electrospinning is capable of producing fibers in the submicron range [98].
In 2009, researchers fabricated conductive nonwoven nanofibers for gas sensing applications by
electrospinning solutions of zinc acetic acid with a poly 4-vinyl phenol. After the combination
fibers were initially formed, zinc oxide (ZnO) nanofibers were obtained by calcinations burning
off the polymer carrier [100]. Electrospinning can also be used to fabricate nanocomposite

24

fibers, removing the need for a separate recognition element immobilization step during
fabrication. Nanocomposite fibers of urease and polyvinylpyrrolidone (PVP) were made into
nonwoven mats for electrochemical sensing of urea [101]. The enzyme / polymer composite
mats had increased surface area and smaller pore sizes, resulting in a larger adsorption rate and
therefore decreased response time for detection. Electrospun fibers have been studied for use in
several different sensor technologies such as: acoustic wave, resistive, photoelectric, optical, and
amperometric [102].

2.3.1.2 Melt extrusion
Another method to create nonwovens involves a melt extrusion process. In polymer melt
extrusion, the hot liquid polymer is forced through an extruding device containing either a mold
or die. It is then cooled until it solidifies into the desired fiber shape [103]. At North Carolina
State University, an elastomer in a coaxial configuration was used to create hollow fibers into
which a specific additive, conductive eutectic gallium indium, was used to fill the fibers to form
medical / diagnostic monitoring products [104]. This process can be tailored to specific
applications based on the additive used.

2.3.2 Surface treatments
2.3.2.1 Drop on Demand
In many electrochemical based sensing systems the nonwoven fibers are made conductive in
order to act as a substitute for a metal electrode. One method for forming conductive nonwoven
fibers is drop on demand ink jet printing [105]. In drop on demand an ink composed of an

25

aqueous carrier, a pigment, and a polymer having either acid, base, epoxy, or hydroxyl functional
moieties is applied to a textile that contains a hydroxyl, amine, amido, or carboxyl group and an
organometallic or isocyanate cross-linking agent [106]. When a current is pulsed through either
a heating element or piezoelectric material associated with the ink chamber it causes the ink to be
propelled out of the nozzle onto the textile in a drop [107]. This method has been used for
developing sensors using nonwovens of polyethylene / polypropylene and can be used to
network conductive components throughout the fabric. Applications usually center on the health
care field, focusing specifically on the monitoring of vital signs such as respiration and
electrocardiograms [108].

2.3.2.2 Atomic layer deposition
Others in the field of conductive nonwovens have looked at utilizing processes such as atomic
layer deposition (ALD), a vapor phase growth process known for its metal oxides, nitrides, and
conducting thin films, where self-terminating gas-solid reactions are performed sequentially to
fabricate conformal coatings as small as the nanometer range [109]. The conformal coating
process for deposition of metal oxides on surfaces can be carefully controlled and tailored for
thickness and content of the metal oxide layers. Plasma-enhanced ALD coating application is
beneficial because it operates at low temperatures, making this method amenable to thermally
sensitive materials, such as polymers [110, 111]. In the work by Jur et al [112] researchers
coated various fibrous textiles with different metal oxide layers and conductivities ranging from
24 S/cm for zinc oxide (ZnO) to 1150 S/cm for tungsten oxide were observed.

26

2.3.2.3 Conjugated polymers
Other methods for creating conductive nonwoven fibers for use in sensors involve the use of
conjugated polymers both within and coating the nonwoven fabric structure. Organic conjugated
polymers in recent years have been well studied and used in such fields as energy storage,
memory devices, electrocatalysts, and electrochemical devices [113]. Conductive polymer
preparation can be straightforward and production of some, such as polyacetylene and
polypyrrole, can be simple to form using electrochemical or chemical synthesis. In order to
achieve electrical conductivity, a charge must be capable of being transferred along the
conjugated chain, between chains or particles, and along grain boundaries. This is achieved
when π-overlap along the polymer chain occurs, providing a half-filled band of delocalized πelectrons [114]. Their unique properties such as stability in air and compatibility with biological
molecules make them ideal candidates for use in biosensing. The conductive polymer
polypyrrole has been one of the most studied polymers for use in biosensing and has been found
to be the most active for both deposition at neutral pH and immobilization of biomolecules [115].
A popular method for conjugating polymers with nanofibers is oxidative chemical vapor
deposition (oCVD), where an electrically conductive coating can be formed directly on the
fibers. This is done by simultaneously combining an oxidant and a monomer in the reaction
chamber with the fiber substrates [116]. This method has shown versatility in the coating of
various substrates from films to fibers. Nylon fibers were coated with the conductive copolymers of polyethylene dioxythiophene / 3-thiopheneethanol using oCVD. These fibers have
recently been studied for use in a resistance based sensor where the immobilization of
biomolecules uses the built in carboxyl (–COOH) functional group of the polymer [13].

27

A two-step combination process has been used to develop nonwoven conductive fibers formed
using polyethylene oxide (PEO) containing the oxidant ferric chloride (FeCl3) [117]. The PEO
fibers containing the oxidant were produced using electrospinning and in the second step the
fibers were exposed to pyrrole vapor for polypyrrole (PPy) synthesis. This same method was
used by Granato et al [12] to develop polypyrrole coated fibers, except the fibers were made of
nylon-6 instead of PEO. In thin film devices that contain PPy, active sensing components are
imbedded which can limit efficiency and sensitivity. A nanofibrous surface improves these
factors because of the high surface area for the given mass / volume. In addition, the nanofiber
platform can enhance the transport of ions and chemicals from the solution to the interior of the
sensor component, perhaps even acting as the capture and sensing component all in one. Results
showed that the composite PPy / PEO fibers were very smooth after exposure to pyrrole vapor
with the average diameter of the fiber being 96 nm. The sheet conductivity of the fibers was
-3

found to be on the order of 10 S/cm [117]. The nylon-6 / PPy fibers were found to have an
average diameter of 290 nm and could be used to measure an amperometric response due to
2

increases in concentrations of a phosphate solution with a significant R of 0.990 [12].

The most largely researched and easiest method for the fabrication of conductive fibers is
through the use of aqueous / organic solvent deposition using chemical polymerization [118].
This can be achieved by using a bulk process where a relatively strong chemical oxidant oxidizes
a monomer in solution and the resulting polymer precipitates out of the solution as a solid. For a
finer, more controlled coating, the monomer or oxidant can be exposed to only the fibers, then
introduced to the reacting solution resulting in the polymerization occurring directly at the

28

surface. Other polymer coated fibers and textiles have been generated using liquid chemical
oxidation to generate PPy [119-124] and polyaniline (PANI) polymers onto fibers [125]. The
resulting electrotextiles maintain the porosity and increased surface area of a fiber textile, but the
polymer coating along the individual fibers are also capable of providing electrical conductivity
through the membrane.

2.3.3 Covalent biosurface modification
The demands of a successful biomaterial immobilization with conductive fibers are: the
biorecognition properties and / or catalytic properties of the bioreceptor remain active; the
recognition elements are attached within or on the substrate to maintain activity; the chosen
biorecognition elements improve selectivity of the biosensor; and the transducer is affected,
whether by increasing or decreasing electron transfer, upon a binding event. In order to
successfully achieve these demands, surface modification of the nanofibers is often necessary.
The strategy for surface modification that is most promising is covalent attachment of
biomolecules, which allows for electron interactions between the active site and the electrode
[126]. Modified electrode surfaces have been examined for use in amperometric biosensors.
Transfer reactions of biological molecules were observed to be normally slow at conventional
metallic electrodes. Bartlett [126] sought to overcome this problem by direct coupling of the
biological redox reactions to the electrodes. This involved the electrodes being modified by
covalent attachment of species, by either reversible adsorption or by deposition of conducting
polymers as electrode materials. Using electrodes coated with conductive poly (5carboxyindole) they were able to use the carboxylate groups on the polymer to interact with the
lysine residues on the protein surface for orientation and electron transfer upon binding events.
29

This study also pointed out the electrochemical constraints of electron transfer in relation to the
distance to the electrode in biosensors.

A chemiresistive biosensor for the detection of the biomolecule biotin was developed using 3,4ethylenedioxythiophene (EDOT) copolymerized with 3-thiopheneethanol (3-TE) on a nylon fiber
mat [13]. The available hydroxyl (–OH) functional group found in the copolymer was
covalently attached to the biomolecule avidin using p-maleimidophenylisocyanate (PMPI)
molecules as a cross-linker. The naturally high affinity constant of avidin for biotin ensured the
sensor would have strong selectivity and specificity. This was confirmed using laser scanning
confocal microscope images of the fibers with FITC-avidin molecules before and after reacting
with biotinylated red quantum dots [13].

2.3.4 Electrochemical detection using nonwoven fibers
Electrochemical based sensing does not require enzyme labels or redox mediators to facilitate
detection like optical sensors do and output signals are directly related to the concentration of
bound antigen to the sensor. Three of the most commonly used electrochemical detection
methods in biosensing are amperometry, voltammetry, and resistivity, or looking at changes in
the current, voltage, or resistance, respectively, across the system [127]. The system parameters
commonly measured are: interfacial capacitance, electron transfer resistance, and medium
conductivity. In developing an electrochemical based biosensor using nonwoven fibers, it is
important to take into consideration such individual components as: the conductivity of the
electrolyte solution, the distance between electrodes, the total electrode surface area, the
electrode materials, and the temperature and pH at which the measurement is carried out [128].
30

Electrically based detection systems that use a polymer nanofiber-based biosensor paired with
enzymes have been developed for sensing glucose [129], urea [101], and fructose [130].
Recently studies have been conducted to use nanofibers as biosensors for detecting food
pathogens. Some examples of this include nitrocellulose nanofibers that have been fabricated and
used as a lateral flow immunosensor for detecting E. coli 0157:H7 [131], Salmonella spp [80],
Bacillus spp [132]. In this section, a more in depth review is made on the use of nonwoven
fibrous platforms for electrochemical biosensors.

2.3.4.1 Cyclic voltammetry
Cyclic voltammetry provides information on the thermodynamics of redox processes, kinetics of
heterogeneous electron-transfer reactions, coupled chemical reactions, or adsorption processes
[75]. It is also used to determine the locations of redox potentials of electroactive species and
evaluate media effects . In cyclic voltammetry, a linear scan of the potential of a working
electrode is performed using a triangular potential waveform. A single cycle or multiple cycles
can be performed. During the potential sweep the current resulting from the applied potential is
measured and the current versus the potential is plotted on to a cyclic voltammogram. Forward
and reverse scans will result in peaks of O to R (oxidation to reduction) and R to O, respectively.
These peaks are the result of the diffusion layer formed near the electrode surface. In reversible
systems the current and potential at each of the two peaks can be used for data analysis with the
current being directly proportional to the concentration of the electroactive species of interest
[75].

31

Arecchi et al. [133] used nylon nanofibrous membranes to build electrochemical biosensing
devices for the detection of glucose that allowed high enzyme loading due to the high surface-tovolume ratio of nanofibers when compared to thin film technology. Glucose oxidase was
attached to the nanofibers using glutaraldehyde to covalently immobilize the enzyme. The
functional membrane was then securely attached to a carbon electrode, completing the
electrochemical biosensor. Cyclic voltammetry was used to analyze the sensor with the nylon
membranes attached. The sensor exhibited a sensitivity of 1.9 µA/mM over a response time of
-6

20 – 30 seconds and had a limit of detection of 6 x 10 M.

2.3.4.2 Amperometry
Amperometry uses the stepping of the potential of the working electrode from where no faradaic
reaction occurs to a potential where the surface concentration of the electroactive species at the
electrode is effectively zero [75]. In this method, the current over time is monitored.

Biosensors used in glucose monitoring have been created using immobilized glucose oxidase
associated with an electrospun electrode made of nanofibers consisting of
polymethylmethacrylate dispersed with multiwall carbon nanotubes. Glucose was detected
amperometrically in a phosphate buffer and effective electron mediation was achieved. A
benefit of this nonwoven fabricated electrode is flexibility for the flow of gas and liquids through
the high surface area allowing higher loading of biomolecules for improving biosensor
performance. Testing looked at how stronger binding efficiency of the biomolecules / enzymes
minimized leaching of the active component during the fabrication process. Results showed that

32

the nanofibrous nonwoven biosensor provided excellent detection levels and wide linear range
response to glucose presence [134].

A tyrosinase-modified electrode was developed to be used as an electrochemical amperometric
biosensor for the detection of phenolic compounds in food [135]. The enzyme had been
immobilized by drop-coating onto a glassy carbon electrode that had been covered with a
nanofibrous membrane made of nylon-6 that was prepared by electrospinning. A three-electrode
system was used with the coated glassy carbon serving as the working electrode. At -0.2 V the
amperometric response was measured, showing a decrease in current over time with the addition
of standard pyrocatechol. The biosensor exhibited a response time of 16 seconds, a detection
limit of 0.05 µM, and linearity up to 100 µM.

2.3.4.3 Resistivity / conductivity
In electrochemical resistance / conductance based biosensors, the biological recognition event
creates either a disruption or connection for the flow of current at the working electrode and a
system response is measured [136, 137]. This resistance can be correlated to different target
pathogen concentrations, resulting in pathogen detection. The change of the system resistance
should be proportional to the change in the amount of pathogen captured. Fabrication of a
chemiresistive biosensor for detection of biomolecules was demonstrated on a high surface area,
flexible electrospun nylon fibrous mat [13]. The –OH functionalized conducting copolymer of
3,4-ethylenedioxythiophene (EDOT) and 3-thiopheneethanol (3-TE) was synthesized and
conformally deposited on the electro-spun mats by oxidative chemical vapor deposition (oCVD).
The –OH functional groups associated with the copolymer were used for covalent
33

immobilization of avidin. Biotin molecules were used as the target analyte-specific molecule.
The sensitivities of avidin immobilized conducting copolymer on electrospun mats were tested
against micro-molar to nano-molar concentrations of biotin in aqueous solutions. Results showed
the sensor response was 6 times higher than when a flat substrate was used. It also significantly
lowered the response time.

E. coli O157:H7 bacteria and bovine viral diarrhea virus (BVDV) detection has been conducted
using electrospun fibers of nitrocellulose [131]. Nanofibers were functionalized to contain
antibodies by glutaraldehyde crosslinking chemistry. Silver electrodes were then fabricated on
the nanofibers. To complete the circuit for detection, those same antibodies were attached to
conductive (polyaniline) magnetic nanoparticles which attached when exposed to target
pathogens in solution. This complex then attached to the modified surface of the membrane
completing the charge transfer between the electrodes. Detection of the targets for the fabricated
device was found to be linear in response to the amounts of analyte in solution. In an 8 minute
detection process sensitivity of the biosensor was 61 colony forming units per milliliter
(CFU/ml) for E. coli and 103 cell culture infectious dose per milliliter (CCID/ml) for BVDV
[131].

2.4 Conclusions / outlook
Although a lot of research activity has gone into the development of biosensors, there is still a
disconnect in getting this technology transitioned to real world with commercially available
systems. The biggest cause for this is the common drawbacks that still exist in current
biosensors such as high cost, low durability, a lack of environmental robustness for in field
34

testing, detection limits that do not reach those of traditional techniques, and a necessity for extra
extraction methods to be performed before use adding to the total detection time. Optical
biosensors have better sensitivity and selectivity than electrochemical systems; however they are
very sensitive to the characteristics of the sample matrix. Electrochemical systems are subject to
high levels of system noise, which can reduce the sensitivity of the sensor. Other drawbacks of
current biosensor technology include the need for chemical labels or reporters, a lack of
multiplexing capabilities, short shelf lives, complex production methods, and a lack of
reproducibility of materials and results [32, 52]. The use of durable nonwoven fibers is one new
field that can be explored to help address some of these issues. Their low cost, material
durability, ease of production, and material stability make them particularly promising for
sensing applications.

There are many different approaches available for the formation and use of nonwoven fibers for
biosensing. Nonwoven fibers can be utilized as optical or electrical based sensors with
appropriate design considerations such as choice of polymer, fiber packing density, biomolecule
attachment chemistries, signal attenuation, dopants, and detector / reader design. Care must be
taken to choose materials that will be able to maintain their optical or conductive properties in
the presence of biological systems. The requirement for small, ultrasensitive, affordable,
disposable sensors for medical diagnostics, environmental monitoring, and food safety has led to
an increase in this research.

35

Chapter 3 : Synthesis and characterization of electrotextile fibers

This chapter is adapted from our recently published work in the journal, Biosensors:
McGraw, Shannon K.; Alocilja, Evangelyn; Senecal, Andre; Senecal, Kris. Synthesis of a
Functionalized Polypyrrole Coated Electrotextile for Use in Biosensors. Biosensors. 2012;
2(4):465-478. DOI: 10.3390/bios2040465.

3.1 Introduction
Electrochemical biosensors combine a biological recognition element with an electrical readout.
There is a large array of biorecognition elements to choose from including: DNA / RNA
aptamers, proteins, antibodies, enzymes, and DNA probes. These biorecognition elements can be
used directly in their natural form or can be biochemically altered [138-141]. Biological
recognition is accomplished when the element is immobilized at an electrode transducer which
converts the biological recognition event (i.e., antibody - antigen binding) into a measurable
electrical signal [54, 75, 142, 143]. Biosensor transducers can be electrochemical, optical,
thermal, mass related, or based on electrical impedance. Impedance based sensing is
advantageous because it does not require enzyme labels or reduction/oxidation mediators to
facilitate detection as optical based sensing does [127]. In electrochemical impedance based
systems a measurable system response is created when the biological recognition event disrupts
the flow of the current at the working electrode while the reference electrode maintains a
constant potential [75].

36

High-surface area nonwoven fibers are versatile and can be developed into electrotextile smart
membranes designed for use with all forms of sensor signal transduction. However, research into
the integration of electrotextile, biological, and electrical technologies to create novel biosensor
systems for food protection is limited. Previously published studies have been conducted on the
development of electrically active non-metallic textile coatings made of doped polypyrrole (PPy)
polymers [13, 14, 19, 121, 122, 144]. By producing a conductive polymer coating on non-woven
microfibers, an electrochemical biosensor electrode can be created that is less expensive than its
planar metal counterpart, with more available surface area [12].

In addition, these electrotextile electrodes can be engineered to be durable, disposable, and
require minimal attachment chemistries. The ability to use antibody functionalized fibers for
capture and concentration was previously demonstrated with electrospun nanofibers and a
carboxyl functional group [19, 145]. With the attachment of biological recognition elements to
the electrotextile surface, these electrodes have the capacity to act as the transducer in a
biosensor while also performing pathogen capture, concentration, and detection [13]. This
combination would simplify a food pathogen detection biosensor, resulting in a significantly
smaller and lighter detection system.

The inclusion of a carboxyl group in the polymerization of such an electrotextile based sensor
would provide the needed functional group sites for the attachment of biorecognition elements
necessary to a biosensor design (i.e., antibodies, avidin). Various types of molecules have
previously been included during the polymerization of pyrrole in order to create biosensors with
built in biological receptor sites, such as biotin [146], benzophenone [147], pyrrole-3-carboxylic

37

acid [148] and 3-thiopheneacetic acid (3TAA) [17]. Rapid, highly specific sensing of target
analytes can be achieved due to the use of these elements at a relatively low cost. The faster
speed and lower cost of biosensors versus standard detection methods have made them especially
marketable to the food industry [149-151].

The objective of this study was to develop and produce an electrotextile with a biosensing focus
composed of conductive polymer coated microfibers that contain functional attachment sites for
biorecognition elements. Experiments were conducted to select a functional group, fiber
platform, and polymerization solvent. The effects of dopant inclusion and post-polymerization
wash steps were also analyzed. Investigations were conducted to determine if the inclusion of
3TAA in the polymerization process would have an effect on the availability of binding sites for
biorecognition elements in the high-surface area electrotextile and how the increase in the
concentration of 3TAA would affect several properties of the coating such as the physical
characteristics, resistivity of the sample, and the availability of binding sites. Finally, the
successful attachment of avidin to the electrotextile was examined as well as the avidin’s ability
to capture biotin (a common biorecognition model). This was evaluated optically and
electrochemically.

3.2 Materials and methods
3.2.1 Materials
Nylon 6 (NY6) and polypropylene (PP) nonwoven microfibers were obtained from North
Carolina State Nonwovens Cooperative Research Institute. The fibers were cut into circular discs
with a diameter of 1.2 cm. The monomer solution contained 98% pyrrole and either 3TAA or
38

pyrrole-3-carboxylic acid (3-COOH), all obtained from Sigma-Aldrich (St. Louis, MO, USA).
Iron (III) chloride (FeCl3), acetonitrile, methanol, and 5-sulfosalicylic acid (5SSA) were also
obtained from Sigma-Aldrich. Covalent attachment of the biorecognition elements was
performed using N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC)
(Sigma-Aldrich) and N-hydroxysulfosuccinimide (sulfo-NHS) (Invitrogen, Carlsbad, CA, USA)
with 50 mM 2-(N-morpholino)ethanesulfonic acid (MES) buffer, pH 6.0, (Thermo Fisher
Scientific, Waltham, MA, USA), fluorescein isothiocyanate (FITC) labeled avidin (Thermo
Fisher Scientific, Waltham, MA), avidin (Thermo Fisher Scientific), biotin (Thermo Fisher
Scientific), and biotinylated quantum dots (Qdot 655 biotin conjugate kit, Invitrogen).

3.2.2 Synthesis
3.2.2.1 Functional group selection
A circular NY6 membrane sample was dipped into a solution of 1 mg/mL 3TAA in pyrrole,
removed, and placed into a reaction vessel where 10 mL of 0.1 M FeCl3 in acetonitrile was
added and allowed to react for 18 hours at room temperature, during which oxidative
polymerization of the pyrrole based monomer occurred [121, 152-156]. The second sample was
dipped into a solution of 0.5 mg/mL 3-COOH in pyrrole, removed, and placed into a container
where 10 mL of 0.1 M FeCl3 in acetonitrile was added and allowed to react for 18 hours at room
temperature. Both samples were removed from their respective solutions and left to dry for 4
hours at room temperature.

39

3.2.2.2 Dopant inclusion and solvent selection
Six polymer samples were generated and evaluated using NY6 as the fiber platform. The fibers
were dipped into a solution of 1 mg / mL 3TAA in pyrrole, removed, and placed into separate
reaction vessels. Two samples each were oxidized with 10 mL of 0.1 M FeCl3 suspended in
acetonitrile, methanol, or deionized (DI) water. A volume of 1 mL of 0.1 M 5SSA was added
immediately after the addition of the FeCl3 to act as a dopant. All samples were allowed to react
for 30 minutes at room temperature. After polymerization, the samples were removed and dried
for 4 hours at room temperature.

3.2.2.3 Post-polymerization treatment
NY6 membrane samples were dipped into a solution of pyrrole containing 1 mg/mL 3TAA,
removed, and placed into an empty container. FeCl3 (10 mL, 0.1 M) was added to each followed
immediately by 5SSA (1 mL, 0.1 M). Samples were allowed to react for 30 minutes. Samples
were subject to 3 conditions: no post-polymerization treatment, a DI water wash, or a DI water
wash and sonication for 5 minutes. Following treatment the samples were dried for 4 hours at
room temperature.

3.2.2.4 Fiber platform selection
A spot melted PP disc was coated with the PPy conductive polymer. The fiber disc was dipped
into a solution of 1 mg / mL 3TAA in pyrrole, removed, and placed into a separate reaction
vessel where polymerization occurred. A volume of 10 mL of 0.1 M FeCl3 in DI water was used

40

as the oxidant and 1 mL of 0.1 M 5SSA was used as the dopant. The sample reacted for 30
minutes at room temperature and was then removed, washed with DI water, and dried for 4 hours
at room temperature. A sample using NY6 as the fiber platform was also prepared with an
identical coating method.

3.2.2.5 3TAA analysis
PP microfiber discs were briefly submerged in a 10% pyrrole and 3TAA (concentrations of 0,
0.1, 1, 10, 20, 50, or 100 mg / mL) solution. The functionalized monomer was absorbed onto the
fiber mat. The wet fiber sample was then removed from the solution and placed in a glass
container for polymerization. FeCl3 (0.1M, 10 mL) was added to the sample to initiate the
chemical reaction while a dopant, 5SSA (0.1M, 1 mL), was simultaneously added. The fibers in
solution were incubated at room temperature for 30 minutes with constant agitation, thereby
ensuring that polymerization occurred on both sides of the mat. The nonwoven fiber sample was
removed from the solution, gently rinsed on both sides with distilled (DI) water, and dried at
room temperature overnight.

3.2.3 Physicochemical characterization
A visual assessment of each sample was conducted using scanning electron microscopy (SEM).
The samples were gold sputter coated (~15 nm thickness) and imaged with a Zeiss EVO 60
scanning electron microscope fitted with an energy dispersive spectroscopy (EDS) attachment
(Carl Zeiss Microscopy, LLC, Thornwood, NY, USA). The images were taken at a setting of
1,024 × 768 pixels with 4× line integration (noise reduction technique). Slow scan speed 8 was
41

used with a spot size of 370 for a measurement beam current of 70 picoamps. The EHT voltage
was 30.0 kV and the working distance was 6 mm, except where noted otherwise. EDS
measurements were performed with 102.4µS amp time for 500 counts at magnifications of 100x.
Electrical resistance measurements of the polymer were taken across the fiber membranes using
a four point probe (Pro-4, Signatone, Gilroy, CA) and a Keithley 2400 Sourcemeter (Keithley
Instruments, Cleveland, OH, USA) after an interval of 10 seconds.

3.2.4 Biological experiments
3.2.4.1 Optical analysis
PPy / 3TAA coated PP membrane discs were prepared according to the method listed in Section
3.2.2.4 and washed with DI water. FITC labeled avidin (FITC-avidin) was attached to the
functionalized membranes through EDC / sulfo-NHS crosslinking. The discs were washed with
distilled water and dried for 10 minutes. A volume of 200 µL of EDC and sulfo-NHS in MES
buffer was added and reacted with gentle agitation for 15 minutes. The discs were washed twice
in MES buffer and 250 µL of FITC-avidin was added to each disc. The discs were reacted with
gentle agitation for 4 hours and were washed with MES buffer. Finally, the discs were washed
three times with phosphate buffered saline (PBS). For 3TAA analysis the samples were read
using a Fluoroskan Ascent microplate fluorometer (Thermo Scientific) and measured for
fluorescence at an excitation wavelength of 490 nm. Emission was measured at 535 nm.
Triplicate readings were taken for each sample and then averaged to obtain an average
fluorescent output value. Imaging was performed using a Zeiss LSM710 confocal microscope
(Carl Zeiss Microscopy, LLC, Thornwood, NY, USA).

42

To visually assess the avidin functionalized fibers’ ability to capture a target molecule a total
volume of 500 µL of biotinylated Qdots was added to the fibers at a 1:400 dilution. The samples
were incubated with agitation for 1 hour at room temperature and were stored at 4 °C overnight.
The samples were washed three times with PBS and then imaged using a Zeiss LSM710
confocal microscope (Carl Zeiss Microscopy, LLC, Thornwood, NY, USA).

3.2.4.2 Electrochemical analysis
PPy / 3TAA coated PP membrane discs were prepared according to the method listed in Section
3.2.2.4, cut into 2 cm × 2 cm squares, and washed with DI water. Avidin was attached to the
functionalized membranes through EDC / sulfo-NHS crosslinking as described in Section
3.2.4.1. Resistance measurements were obtained by connecting the avidin attached membrane
discs to a PalmSens using two alligator clips on opposing sides of the membrane. The membrane
was submerged in 10 mL of 0.1 M phosphate buffer (PB) and then a 10 mL sample of biotin
solution at varying concentrations was added. The resistance values were recorded every 30
seconds for 15 minutes. The system response (Rp) was calculated using Equation 3-1 [13].
Rp(%)=((R1-R0)/R0 )×100

(3-1)

Where R1 is the resistance of the avidin attached sensor after the biotin sample has been added.
R0 is the resistance of the avidin attached sensor that has not been exposed to biotin.

3.3 Results and discussion
Table 3-1 summarizes the different treatments, platforms, and reaction times that were examined
in this manuscript as well as their impact on material resistance and coating morphology.
43

Analysis of the results from each tested combination is explained in the subsequent sections.
Supplementary data, calculations, and images can be found in Appendix B.1.

44

Table 3-1. Summary of treatments and effects. All reaction times are 30 minutes except for the
samples from Figure 3-1 and Figure 3-2 which had reaction times of 18 hours.
Materials and Methods Tested
Results
Platform Monomer Dopant
Solvent
Wash
Resistance Figure
Notes
Treatment
#
NY6
3-COOH/
none
acetonitrile
none
397 k
3-1
Coating
Pyrrole
even,
black, and
conformal
on fibers.
Heavy
buildup of
polymer
clusters
along
fibers.
NY6
3TAA/
none
acetonitrile
none
23.71 k
3-2
Coating
Pyrrole
even,
black, and
conformal
on fibers.
Clusters of
polymers
buds
scattered
along
fibers.
NY6
3TAA/
none
acetonitrile
none
189.98 k
none Coating
Pyrrole
uneven,
gray, and
brittle.
NY6
3TAA/
5SSA acetonitrile
none
291 k
none Coating
Pyrrole
uneven,
gray, with
dark black
spots,
brittle.
NY6
3TAA/
none
methanol
none
35.31 M
none Coating
Pyrrole
uneven,
gray,
slight
brittleness.

45

Table 3-1 (cont’d).
NY6

3TAA/
Pyrrole

5SSA

methanol

none

710

3-4

NY6

3TAA/
Pyrrole

none

water

none

557

none

NY6

3TAA/
Pyrrole

5SSA

water

none

91.5 ,
51.2

3-5
3-6

NY6

3TAA/
Pyrrole

5SSA

water

DI water
wash and
sonication

60.5

3-8

46

Coating
uneven
and gray
with black
spots.
Polymer
forms a
solid sheet
across
fibers.
Coating
smooth
and even,
black,
slightly
brittle
Coating
smooth,
even, and
black.
Polymer
clusters
are small
and build
along fiber
surfaces
Coating
smooth,
even, and
black.
Coating is
slightly
lighter
than other
samples
with better
porosity

Table 3-1 (cont’d).
NY6

3TAA/
Pyrrole

5SSA

water

DI water
wash

42.6

3-7
3-9

PP

3TAA/
Pyrrole

5SSA

water

DI water
wash

55.1

3-10

Coating
smooth,
even,
black, and
slightly
brittle.
Coating is
slightly
lighter
than
unwashed
sample
Coating
smooth,
even, and
black. Not
brittle.
Coating is
conformal
along
fibers.

3.3.1 Functional group selection
The inclusion of a pendant carboxyl functional group associated with the conductive polymer
coating provides attachment sites for the covalent binding of antibodies to the fibers, giving a
biosensor its ability to detect pathogens and specificity of capture [157]. The groups 3TAA and
3-COOH were evaluated as potential functional group additions in the electrotextile polymer.
The SEM images of the samples can be seen in Figures 3-1 and 3-2 and the results can be seen in
Table 3-1.

47

Figure 3-1. SEM image of nylon 6 fibers coated in polypyrrole with 3 COOH. Scale bar
3-COOH.
equal to 10 µm, magnification of 2,000x, EHT of 30 kV and working distance of 6.0 mm.

48

Figure 3-2. SEM image of nylon 6 fibers coated in polypyrrole with 3TAA. Scale bar
.
equal to 20 µm, magnification of 2,000x, EHT 30 kV, and working distance of 6.0 mm.

The sample using 3TAA formed an even black coating across the fiber surface. SEM analysis
showed that the coating was conformal on the individual fibers on the membrane with clusters of
owed
polymer buds scattered along the fibers. The measured resistance for the sample was 23.71 k .
The sample with 3-COOH additive had an evenly dispersed black coating across the surface as
COOH
coating
well. SEM analysis showed that the fibers were conformally coated, however the buildup of
polymer clusters on the fibers was much heavier than in the sample where 3TAA was used as the
additive. This buildup of polymer on the sample using 3-COOH pyrrole caused an increased
resistance of 397 k for the sample. In the development of an electrotextile electrode, it is
important to minimize material resistance and for the conductive polymer coating to be
continuous throughout the fibrous platform. This ensures consistency across the electrode surface
t
for recognition element attachment and that any change in electrical signal is due to target
49

binding to the recognition site instead of variations between fabricated electrodes. Based on this
information, 3TAA was selected as the functional group additive to be used in the
polymerization for the remaining experiments.

The chemical structure of the poly(pyrrole-3TAA) copolymer can be seen in Figure 3-2.

COOH
H
N
S
x

1-x
n

Figure 3-3. Schematic representation of the chemical structure of
poly(pyrrole-3TAA).

Previous work has been done exploring the polymerization of pyrrole with additional molecules
added to create a co-monomer in order to build biological receptor sites into the polymer. These
include biotin [146], benzophenone [147], 3-COOH [148] and 3TAA [17]. The structure is the
same as that published in Vaddiraju et al. [17], however because the deposition method is
aqueous instead of oCVD there are differences in the coating thicknesses, morphologies, and
conductivities. The addition of an organic acid dopant will also affect these parameters.

50

3.3.2 Dopant inclusion and solvent selection
The inclusion of the dopant 5SSA was evaluated as a result of previous research indicating that
the use of planar dopant ions increases conductivity in PPy coatings [14, 122, 158]. The effect of
the polymerization solvent was evaluated as well. These results can be seen in Table 3-1.

The samples with acetonitrile as a solvent were both unevenly coated, with the sample
containing dopant having dark black spots across the surface. The coating was very brittle. As
shown in Table 3-1, the measured resistance for the sample oxidized in acetonitrile with 5SSA
was 291 k . The resistance of the sample without the inclusion of the dopant was 189 k . The
samples where methanol was used as the solvent had uneven black coatings. The sample that did
not have 5SSA added had a measured resistance of 35 M , however the sample where 5SSA
was added had a measured resistance of 710 .

The samples that were oxidized using FeCl3 in water had smooth and even black coatings. The
sample without 5SSA had a slightly heavier surface coating and appeared more brittle. The
sample without 5SSA had a measured resistance of 557 . The sample with 5SSA had a
resistance of 91.5 .

The samples containing 5SSA that were oxidized in methanol and in water were selected for
further evaluation using SEM. These images can be seen in Figures 3-4 and 3-5.

51

Figure 3-4. SEM image of nylon 6 fibers coating in doped polypyrrole with methanol as
.
solvent. Scale bar equal to 20 µm, magnification of 2,000x, EHT of 30 kV and working
distance of 6.0 mm.

52

Figure 3-5. SEM image of nylon 6 fibers coated in doped polypyrrole with water as
.
solvent. Scale bar equal to 10 µm, magnification of 2,000x, EHT of 30 kV, and working
kV,
distance of 8.0 mm.
The sample oxidized using methanol had a less globular appearance than previously seen in the
mple
acetonitrile samples without the dopant, however it appeared more like a solid sheet of coating
t
across the fibers. In comparison, the sampl that were oxidized in water were very globular, the
samples
re
polymer clusters seen previously were present, but much smaller and building along each
lymer
were
individual fiber.

When comparing the samples shown in Figure 3-4 and 3-5 with the samples from Figures 3-1
Figures
Figure
and 3-2, it can be seen that the choice of solvent for the reaction was shown to affect polymer
,
formation on the fiber surface, directly relating to overall polymer conductivity. Th inclusion of
The
a dopant resulted in increased conductivity across the fiber membranes in less reaction time. The
inclusion of the dopant had the greatest impact on conductivity when the conductive polymer
53

was chemically oxidized in water resulting in the lowest sample resistance among the tested
combinations.

3.3.3 Post-polymerization treatment
In order to evaluate the strength of the attachment of the coating to the fiber surface, a wash step
and sonication were introduced post-polymerization to remove excess, unattached polymer. Each
cleaning step was tested for effect on resistance. Untreated, washed, and sonicated samples were
measured using a four point probe to determine the resistance. These results can be seen in Table
3-1. Their resistances were 51.2 , 42.6 , and 60.5 , respectively. With a range of 17.9 , the
difference observed between the resistance measurements of the three samples was minimal and
did not appear significant.

SEM analysis, shown in Figures 3-6, 3-7, and 3-8, shows heavy, clustered polymer coatings
along the fibers from each sample, with the sample that was not washed appearing to have a
slightly heavier surface coating.

54

Figure 3-6. SEM image of nylon 6 fibers coated in doped polypyrrole with no rinse post
postpolymerization. Scale bar equal to 100 µm, magnification of 500x, EHT of 30 kV and
working distance of 6.0 mm.

55

Figure 3-7. SEM image of nylon 6 fibers coated in doped polypyrrole with a DI water
.
wash post-polymerization. Scale bar equal to 100 µm, magnification of 500x, EHT of 30
polymerization.
kV and working distance of 6.0 mm.

56

Figure 3-8. SEM image of nylon 6 fibers coated in doped polypyrrole with DI water
wash and sonication post-polymerization. Scale bar equal to 100 µm, magnification of
polymerization.
500x, EHT of 30 kV and working distance of 6.0 mm.
The addition of a rinse step and sonication did not result in a significant loss of polymer coating
from the fiber discs, however those samples did show better porosity between the individual
fibers in the SEM images. The lack of a change in resistance and SEM images indicated that the
change
polymer had bound to the nylon microfiber lattice. The larger clusters of polymer, where the
polymer was attached to itself as opposed to the fiber surface, had weaker bonds, was removable
and did not significantly change the resistance. Washing of the fibers post polymerization was
ificantly
post-polymerization
added to the protocol to allow for the removal of weakly bound excess, resulting in better fiber
porosity and ensuring that the biorecognition elements would have access to the lower layers of
the fiber mat.

57

3.3.4 Fiber platform selection
During drying the coated NY6 fibers contracted, resulting in the disc becoming slightly smaller
discs
in diameter than before polymerization occurred. The coated fibers also became more brittle,
occasionally fracturing when bent or twisted. To address this effect, a spot melted PP disc, a
more robust material, was coated with the PPy conductive polymer using the procedure
he
described in Section 3.2.2.4 in a 30 min reaction and compared to an identically coated NY6
2.2.4
membrane. After being washed with DI water SEM images of the samples were taken. These
results can be seen in Table 3-1 and Figure 3-9 and 3-10.
Figures

Figure 3-9. SEM image of nylon 6 fibers coated in doped polypyrrole. Scale bar at 10
.
µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

58

Figure 3-10. SEM image of polypropylene fibers coated in doped polypyrrole. Scale bar
.
at 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

The PP disc in Figure 3-10 has an even black coating and a measured resistance of 55.1 . The
PP microfibers were conformally coated with the PPy polymer. The surface coating on the PP
re
fibers appears smoother than the coating on the NY6 fibers. Nanoscale buds of polymer were
ers.
we
seen scattered across the polymer surface ranging in size from 0.5 to 1 µm. At the a
areas where
the nonwoven fibers were melted together, buds of polymer coating are seen in the range of 200
re
to 400 nm in diameter. It was observed that t coated PP discs also have better durability than
the
dura
its NY6 counterpart. The PP discs were able to be folded, rolled, and handled with less fracturing
and loss of coating.

59

3.3.5 Effects of 3TAA concentration
Increases in resistivity, sulfur weight percent, and fluorescent output were all observed as the
concentration of 3TAA increased in the samples. This increase became pronounced at a
concentration of 10 mg/mL of 3TAA in the monomer solution. A summary of results can be
found in Table 3-2.

Table 3-2. Characterization of polypyrrole copolymer with increasing concentrations of 3TAA.
Concentration of
3TAA
(mg / mL)
0
0.1
1
10
20
50
100

Average
Resistivity
( ·cm)
4.55
4.58
3.43
6.29
8.14
9.44
1587.45

Sulfur
Weight
(%)
0.93
0.78
0.55
1.30
1.54
2.24
3.83

Fluorescent Output
Average
(RFU)
1.0287
1.0950
1.3870
1.4770
1.2677
1.6453
3.9623

3.3.5.1 SEM analysis
The increase in concentration of 3TAA in the polymerization process resulted in an increase in
the buildup of the coating on the PP fibers. Little visible difference was observed between the
samples ranging in concentration from 0 – 10 mg / mL. The four samples tested within this
range showed a conformal polymer coating around the individual PP fibers. A comparison of
two fiber samples with different concentrations of 3TAA can be seen in Figures 3-11 and 3-12.

60

Figure 3-11. SEM image of fibers with polymer coating with a 3TAA concentration of
.
10 mg/mL. A smooth conformal polymer coating was observed along the individual
fibers with minimal polymer clusters. Scale bar equal to 10 µm, magnification of 5,000x,
magnification
EHT of 30 kV and working distance of 6.0 mm.

61

Figure 3-12. SEM image of fibers with polymer coating with a 3TAA concentration of
.
100 mg/mL. The coating was rough with a large amount of polymer built up along the
up
surface, engulfing several fibers and reducing the porosity of the membrane. Scale bar
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

Along the fibers small buildup of polymer could be observed. An example of this can be seen in
f
Figure 3-11, where fibers were coated with a concentration of 10 mg/mL. Samples with higher
,
3TAA concentration displayed large buildups of polymer that had collected together to form
aggregates measuring roughly 400 – 500 µm in diameter on the fiber surface. As seen in Figure
3-12, fibers coated at a concentration of 100 mg/mL show polymer build up along the surface,
,
engulfing several fibers and reducing the porosity of the membrane instead of forming a smooth
conformal polymer coating along the individual fibers. It was also observed that as the
concentration of 3TAA was increased, the polymer coating became more brittle. Flakes of
polymer fell off of the samples containing 50 and 100 mg/mL when handled.

62

3.3.5.2 Electrical resistivity
As can be seen in Table 3-2, the measured resistivities of the samples ranged from 3.43 to 1587.5
•cm. The relationship between the concentration of 3TAA in each sample and the resistivity of
the sample can be seen in Figures 3-13 and 3-14.

Average Resistivity (ohm x cm)

3500
3000
2500
2000
1500
1000
500
0
0.0

0.1
1.0
10.0
20.0
50.0
Concentration of 3TAA (mg/mL)

100.0

Figure 3-13. Change in sample resistivity based on increasing 3TAA concentrations,
with error bars representing the standard deviation of each sample. The overall trend
showed resistivity increasing as concentration of 3TAA increases, starting at 1 mg/mL.

63

Average Resistivity (ohm x cm)

12
10
8
6
4
2
0
0.0

0.1
1.0
10.0
20.0
Concentration of 3TAA (mg/mL)

50.0

Figure 3-14. Change in sample resistivity based on increasing 3TAA concentrations
excluding 100 mg/mL 3TAA, with error bars representing the standard deviation of each
sample.

The samples containing 0, 0.1, 1, 10, 20, and 50 mg/mL of 3TAA all have resistivities under 10
•cm, with the samples ranging from 0 to 1 mg/mL under 5 •cm. A sharp increase was
observed in the resistivities of the samples containing 100 mg/mL of 3-TAA, with the resistivity
being over 150 x larger than the sample containing 50 mg/mL. A steady increase in resistivity
was observed as the concentration increased starting at 1 mg/mL.

3.3.5.3 Elemental weight percent
Because the chemical structure of 3TAA contains a free carboxyl attached to a sulfur ring, the
presence of sulfur was used as an indicator of carboxyl groups present in the coating surface for
covalent binding, this relationship can be seen in Figure 3-3. Energy dispersive spectroscopy
64

was used to determine the elemental weight percentages for each sample. The sulfur weight
percent measured in each sample can be seen in Table 3-2 ranging from 0.55% to 3.83%. The
relationship between the weight percent of sulfur and the concentration of 3TAA in each sample
can be seen in Figure 3-15.
4.5

Sulfur Weight (%)

4
3.5
3
2.5
2
1.5
1
0.5
0
0.0

0.1

1.0
10.0
20.0
50.0
Concentration of 3TAA (mg/mL)

100.0

Figure 3-15. Change in sulfur weight percent at 100x magnification using EDS based on
increasing 3TAA concentrations. The overall trends shows the sulfur weight percentage
increasing as the concentration of 3TAA increases in the sample, starting at 1 mg/mL.
All of the samples with 3TAA concentrations of 10 mg/mL or higher had a sulfur weight percent
of greater than 1%. The range between the measurements of the samples containing 10 and 20
mg/mL of 3TAA is 0.24%. A sharp increase in the weight percent of sulfur was observed
between the samples containing 50 and 100 mg/mL of 3TAA with a range of 1.59%.

3.3.5.4 Fluorescence
The intensity of the FITC signal measured following the crosslinking reaction was used as an
indicator of the relative amount of avidin that was successfully attached to the available binding
65

sites provided by the presence of carboxyl groups in the polymer coating. The average
fluorescence output for each sample can be seen in Table 3-2. The relationship between the
average fluorescent readout value and concentration of 3TAA in each sample can be seen in

Average Fluorescent Output (RFU)

Figure 3-16.

7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0.0

0.1

1.0
10.0
20.0
50.0
Concentration of 3TAA (mg/mL)

100.0

Figure 3-16. Change in average fluorescent output after FITC-avidin binding based on
increasing 3TAA concentrations with error bars showing the standard deviation for each
sample. The overall trend shows the average fluorescence output increasing as the
concentration of 3TAA increases in the sample, starting at 0.1 mg/mL.

The average fluorescence signal measured range from 1.0287 to 3.9623 relative fluorescence
units (RFUs). Only the samples containing 0 and 0.1 mg of 3TAA measure below the value of
1.1 RFUs. The samples containing 50 and 100 mg/mL both exceed 1.5 RLUs. The sharpest
increase in signal comes between the samples containing 50 and 100 mg/mL with the difference
being 2.317 RFUs. The increase in concentration of 3TAA in each sample coincide with an
increase in fluorescent signal for every sample except between 10 and 20 mg/mL.
66

3.3.5.5 Selecting 3TAA concentration
In all 4 characterization methods used, a difference was observed between samples that did and
did not include 3TAA. The trend was observed for each characterization that after an initial
threshold was met, the measured difference increased as the concentration of 3TAA increased.
The addition of 3TAA resulted in an increase in the size and buildup of the polymer coating
along the individual polypropylene fibers, with the largest accumulation of polymer observed
with the addition of 100 mg/mL of 3TAA. The addition of 3TAA also resulted in a higher
resistivity being measured for the sample, a higher elemental weight percent of sulfur in the
sample, and a higher fluorescent reading after the samples were put through the EDC / sulfoNHS / FITC-avidin binding reaction. The additional carboxyl groups from the 3TAA reacted
with the EDC / sulfo-NHS cross-linking to result in this increase in the attachment of the FITC
labeled avidin. However, the increase in available functional groups for additional antibody
attachment came at a cost to the material’s resistivity. Increasing 3TAA changed the polymer
morphology ultimately resulting in a larger, more globular, and less conductive coating.

When developing an electrochemical biosensor, it is important that a balance be found between
increasing the available binding sites for reactions to take place and decreasing the membrane
sample resistivity to achieve maximum sensitivity. It is also important that the membrane
maintain its porosity and therefore increased surface area as well as be environmentally robust.
In our research no observable difference could be found between a sample containing 0.1 mg of
3TAA and one that did not have 3TAA added. A Student’s t-test (2 tail, α = 0.05) showed no
significant difference between the resistivities of the samples containing 0 and 0.1 mg/mL of
3TAA. In all samples tested, the concentration vs. signal outputs showed an observable

67

difference between a sample with a 3TAA concentration at 10 mg/mL and a sample without
3TAA. At a concentration of 10 mg/mL, the membrane sample had a resistivity 38.3% higher
than the sample containing no 3TAA, a sulfur weight percentage 39.8% higher, and an increase
in fluorescence output of 43.6%. Increases in sulfur weight percents and fluorescence outputs
were also observed at concentrations of 20, 50, and 100 mg/mL, however all three showed an
increase in polymer buildup on the fibers which resulted in a lower polymer durability and
higher sample resistivity. For these reasons, 10 mg/mL of 3TAA was selected as the optimum
tested concentration for use in our electrotextile biosensor assembly.

3.3.6 Biorecognition element attachment
3.3.6.1 Optical analysis
Generating a conductive polymer coating onto the fiber membranes has two purposes. The first
is to make the fibers capable of conducting an electrical signal through the fibrous platform and
the second is to provide attachment sites for biorecognition elements on these fibrous surfaces.
Confocal microscopy was used to determine if FITC labeled avidin was covalently bound to the
polymer coating. Qdot labeled biotin was then used to indicate if the surface bound avidin had
maintained its capture ability. Figures 3-17 and 3-18 confirm the presence of functional groups
for bio-recognition attachment in the polymer. Figure 3-17 shows a coated fiber that has FITClabeled avidin attached to it.

68

A

B

C

Figure 3-17. False colored confocal images of single fibers with FITC-avidin attachment. A:
Fiber reflectance. B: Bound FITC-avidin. C: Composite image. All at 4,000× with lasers at 405
and 488 nm.
The fiber can be seen reflecting the blue wavelength, while the FITC-avidin fluoresces green.
The composite image, Figure 3-17(C), shows the attachment of the avidin on the fiber surface,
with a buildup of avidin found on the polymer buds that run along the fiber. This indicates that a
biorecognition element, avidin, can be successfully bound to the polymer coating using covalent
attachment chemistry, however it does not prove that the avidin is still available to react with a
sample and perform capture.

Figure 3-18 shows the same fiber sample, after the addition of Qdot labeled biotin.

69

A

B

C

Figure 3-18. False colored confocal images of fibers with biotinylated quantum dot attachment.
A: Fiber reflectance. B: Bound biotinylated quantum dots. C: Composite image. All at 4,000×
with lasers at 405 nm.
The fibers can be seen reflecting the blue wavelength, while the biotinylated Qdots emit red.
The composite image, Figure 3-18(C), shows the attachment of the Qdots on the fiber surface,
with heavier concentrations of Qdots found where there are heavier concentrations of polymer.
Based on the information gained from Figure 3-17, it can be assumed that the avidin has bound
to the polymer coating and then conjugated with the biotin. This indicates that a biorecognition
element, avidin, can be successfully bound to the polymer coating using covalent attachment
chemistry and can be used to perform capture.

3.3.6.2 Electrochemical analysis
Preliminary experiments were conducted taking multiple measurements using the fiber
membranes as electrodes to determine if a biological recognition signal can be observed.
Triplicate measurements were taken using the conductive fiber membrane electrodes to establish
the resistance values for a control sample (0.1 M PB) and biotin solutions at concentrations of
0.5, 5, 50, and 500 µM. A time of 3 minutes was determined to be necessary to reach system

70

equilibrium. The responses for each concentration at each time point were averaged and the

Response (%)

value after the initial 3 minute equilibrium time can be seen in Figure 3-19.

100
90
80
70
60
50
40
30
20
10
0

0.5 µM
5 µM
50 µM
500 µM

3.0

8.0
Time (minutes)

13.0

Figure 3-19. Average response over a period of 12 minutes (after a 3 minute equilibrium
time) for the electrochemical detection with varying biotin solution concentrations.

The response of the system at each sample concentration was plotted over the 12 minute period
following a 3 minute equilibrium time, showing that the system response increases as the
concentration of biotin increase. There was a slight drop in signal between 3 and 5 minutes for
each sample indicating that the electrode was still reaching equilibrium up to roughly the 5
minute point. The average percent response at a concentration of 0.5 µM was 35.4%. For the
biotin concentrations of 5, 50, and 500 µM the average responses were 55.3%, 50.8%, and
67.2%, respectively. Figure 3-19 shows that the average responses at concentrations of 5 µM, 50
µM, and 500 µM were all significantly larger than the average response at 0.5 µM. Also, the
average responses at these three concentrations are very close, with the average response at 5 and
50 µM crossing multiple time points. This is most likely due to the fibers reaching a threshold
71

for attachment on the surface, so that the increase of biotin in the sample is no longer generating
a proportional increase on the system resistance. The surface attachment capabilities could be
improved by increasing the size and therefore surface area of the fiber mats, attaching a higher
concentration of avidin to the fibers, and increasing the amount of carboxyl group attachment
sites on the fibers.

The resistance of the sensor at each biotin solution concentration was tested against the values of
the control using a Student’s t-test (2 tails, α = 0.05) to determine significance. The resistance at
each concentration of biotin tested was determined to be significantly different from the blank
control. This shows that the electrotextile electrode is capable of detecting changes in
conductivity due to the addition of biotin to the system. It also has the potential to eventually be
used as a simple sample capture and read system for pathogen detection.

3.4 Conclusions
The initial results from this study show a nonwoven fiber matrix can be successfully coated in a
conductive, functionalized polymer while still maintaining surface area and fiber durability.
A polypropylene fiber platform with a conductive polypyrrole coating using FeCl3 as an oxidant,
water as a solvent, and 5SSA as a dopant exhibited the best coating consistency, material
durability, and lowest resistance. The addition of 3TAA to the polymerization process resulted
in a change of coating morphology, resistivity, elemental sulfur presence, and available binding
sites for biorecognition elements. The polymer coated membrane sample containing 10 mg/mL
of 3TAA was selected as the best for future biosensor development. Furthermore, biological
attachment using avidin-biotin can be achieved on the fibers through the inclusion of a carboxyl
72

functional group via 3TAA in the monomer. When put into a simple electrochemical system, the
membranes could be used to successfully detect biotin in solution at concentrations of 0.5, 5, 50,
and 500 µM. This technology will be extremely useful in the formation of electrotextiles for use
in biosensor systems.

A mathematical model of this polymerization process was developed and optimized in Chapter 4.
This technology was further used for antibody attachment and E. coli O157:H7 detection as part
of an electrochemical biosensor in Chapters 5 and 6, respectively.

73

Chapter 4 : An optimization model for the development of a conductive polymer coated
nonwoven electrotextile for use in biosensors

4.1 Introduction
A biosensor is an analytical device used to convert the occurrence of biological recognition
events into measurable electrical responses [54]. It consists of a biological sensing element that
has been integrated with electronic transducers [142, 159]. Biosensor transducers can be
electrochemical, optical, thermal, or mass related. Electrochemical biosensors are designed to
combine the analytical power of electrochemical techniques with the specificity and binding
efficiency of biological recognition processes [75]. In an electrochemical biosensor the
biological recognition element is immobilized at an electrode which then converts the biological
recognition event (e.g. antibody – antigen binding) into a measurable electrical signal. This
output signal will be directly related to the concentration of bound antigen to the sensor.

One new field of research in the development of electrochemical biosensors is the creation of
electrotextiles to be used as electrode transducers. “Electrotextiles refer to fabrics that can
electrically function as electronics and physically behave as textiles” [160]. High-surface area
electrospun membranes are versatile and can be developed into electrotextile “smart membranes”
designed for use with all forms of sensor signal transduction, however very little research has
been done into the integration of electrotextile, biological, and electrical technology to create
novel biosensor systems for food defense. Previous work has been done to develop electrically
active non-metallic textile coatings that are conductive using doped polypyrrole (PPy) polymer
[14, 121, 122, 144]. By producing a conformal conductive polymer coating on non-woven
74

microfibers an electrochemical biosensor electrode can be created that is less expensive and has
more surface area for attachment than its planar metal counterparts [12]. In addition these
electrotextile electrodes are durable, disposable, and have the potential for minimal required
attachment chemistry. With the attachment of biological recognition elements to the
electrotextile electrode surface the electrodes would have the capacity to perform pathogen
capture, concentration, and detection. This would simplify a food pathogen detection biosensor,
allowing the overall system to be produced significantly smaller and lighter than many current
systems.

Previous work has been conducted and published on the creation of an electrotextile made by
performing aqueous deposition of a conductive PPy and 3-thiopheneacetic acid (3TAA)
copolymer onto nonwoven polypropylene (PP) microfibers [18-20]. This combination of copolymer provides sites where bio-recognition elements (antibodies) can covalently attach to the
fiber based platform. These functionalized conductive membranes serve as the sites of pathogen
collection and detection within the system. Captured pathogens impede the flow of electrons
across the electrode resulting in an increased resistance in the circuit. The measured resistance
across the electrotextile electrode increases as the concentration of target pathogen within the
sample increases [20].

There are many factors that influence the formation of this electrotextile. Several of these factors
have been previously identified and analyzed [18]. Of these, five have been identified as
particularly significant to the formation and resistance of the polymer coating: the concentration
of each co-monomer, the concentration of the oxidant, the concentration of the dopant, and

75

polymerization time. It is important in the development of an electrotextile electrode for
biosensing that the electrotextile have the lowest resistance possible in order to not mask small
observable changes that may be obscured with system noise. By decreasing the resistance and
variability of the electrotextile, the sensitivity of the biosensor will increase.

Despite the fact that previous work has identified the importance of five factors in the formation
of conductive textiles [14, 18, 121, 122], a comprehensive study is needed to investigate the
effects of these factors and their interactions in polymer formation. Multiple regression analysis,
a tool often used for estimating probabilities and relationships between variables in fields as
diverse as economics to microbiology to psychology [161-164], can be used to generate a
mathematical model of the electrotextile resistance dependent on the factors of monomer,
oxidant, and dopant concentrations as well as polymerization time.

The objectives of this study included: first, investigating the effects of each monomer
concentration, oxidant concentration, dopant concentration, and reaction time on the synthesized
electrotextile’s resistance; second, developing a multiple regression model based on these
effects; third, determining the optimal conditions of these factors in order to generate the lowest
experimentally possible electrotextile resistance; and finally, experimentally validating the
optimized model conditions.

76

4.2 Materials and methods
4.2.1 Materials
Nonwoven polypropylene microfibers were obtained from North Carolina State Nonwovens
Cooperative Research Institute. For polymer synthesis, the monomer used was a pyrrole solution
that was copolymerized with carboxylic acid functional 3TAA. The oxidant was iron (III)
chloride (FeCl3). The polymer was doped using 5-sulfosalicylic acid (5SSA). All of the
polymerization chemicals were obtained from Sigma-Aldrich (St. Louis, MO).

4.2.2 Experimental design
4.2.2.1 Electrotextile synthesis
Electrotextile synthesis was conducted according to previously published techniques [18-20].
Briefly, an aqueous deposition process was used to create a conductive and functional polymer
coating upon a PP fiber matrix, as described below. A 6 cm x 8 cm polypropylene microfiber
mat was submerged in a pyrrole and 3TAA solution (pyrrole and 3TAA concentrations were
varied based on different experiments mentioned in the ensuing sections), creating a
functionalized monomer that was absorbed onto the fiber mat. The wet fiber sample was then
removed from the solution and laid flat in a glass reaction vessel for polymerization. A volume
of 30 mL of FeCl3 was added to the sample to initiate the chemical reaction while a dopant, 3
mL of 5SSA, was simultaneously added (the concentrations of FeCl3 and 5SSA were varied
based on experiments described in ensuing sections). The fibers in solution were incubated at
room temperature (the reaction times were varied based on experiments mentioned in ensuing

77

sections) with constant agitation, thereby ensuring that polymerization occurred on both sides of
the mat. The nonwoven fiber sample was removed from the solution, gently washed on both
sides with deionized (DI) water, and dried at room temperature overnight.

4.2.2.2 Optimization of electrotextile
Optimization experiments were fulfilled by a response surface method, where the response
surface plot is a 3D representation of the relationship between the factors and the response. The
optimum response will fall somewhere in the experimental region along the generated curved
surface at a maxima or minima [165]. In order to generate this curve based on the effects of the
concentrations of pyrrole, 3TAA, FeCl3, and 5SSA as well as reaction time on the final
resistance of the electrotextile, a central composite design with five coded levels was used. This
was used to develop a mathematical model which was able to predict the electrotextile resistance
as a function of component concentrations and time. The design matrix with both codes and real
values of factors is presented in Table 4-1 [165].

78

Table 4-1. Central composite design, from Haaland. Code values are in the parentheses, actual
values are outside of the parentheses.
Run
Factors

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

Pyrrole
Concentration
(% volume)
32.5 (-1)
32.5 (-1)
32.5 (-1)
32.5 (-1)
32.5 (-1)
32.5 (-1)
32.5 (-1)
32.5 (-1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
77.5 (+1)
10 (-2)
100 (+2)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)
55 (0)

3TAA
Concentration
(g/mL)
0.0125 (-1)
0.0125 (-1)
0.0125 (-1)
0.0125 (-1)
0.0375 (+1)
0.0375 (+1)
0.0375 (+1)
0.0375 (+1)
0.0125 (-1)
0.0125 (-1)
0.0125 (-1)
0.0125 (-1)
0.0375 (+1)
0.0375 (+1)
0.0375 (+1)
0.0375 (+1)
0.025 (0)
0.025 (0)
0 (-2)
0.05 (+2)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)
0.025 (0)

FeCl3
Concentration
(M)
0.075 (-1)
0.075 (-1)
0.125 (+1)
0.125 (+1)
0.075 (-1)
0.075 (-1)
0.125 (+1)
0.125 (+1)
0.075 (-1)
0.075 (-1)
0.125 (+1)
0.125 (+1)
0.075 (-1)
0.075 (-1)
0.125 (+1)
0.125 (+1)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)
0.05 (-2)
0.15 (+2)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)
0.1 (0)

5SSA
Concentration
(M)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.15 (-1)
0.35 (+1)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)
0.05 (-2)
0.45 (+2)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)
0.25 (0)

Reaction
Time
(min)
97.5 (+1)
52.5 (-1)
52.5 (-1)
97.5 (+1)
52.5 (-1)
97.5 (+1)
97.5 (+1)
52.5 (-1)
52.5 (-1)
97.5 (+1)
97.5 (+1)
52.5 (-1)
97.5 (+1)
52.5 (-1)
52.5 (-1)
97.5 (+1)
75 (0)
75 (0)
75 (0)
75 (0)
75 (0)
75 (0)
75 (0)
75 (0)
30 (-2)
120 (+2)
75 (0)
75 (0)
75 (0)
75 (0)

The experiments were run based on the conditions described in the design matrix. The response
was the resistance of a 1.2 cm diameter sample of the electrotextile. Each run was conducted in
79

triplicate with 3 sample measurements taken per experiment, resulting in 9 response values per
run. These values were averaged together to provide each run response and 270 data points for
regression analysis. Optimal conditions for the creation of an electrotextile with the lowest
resistance were obtained from the model. A verification run was then conducted using the
optimal conditions to assess the level of accuracy of the predicted result.

4.2.3 Statistical analysis
The conductivity of the electrotextile from the central composite design was analyzed by
multiple regression analysis using Minitab 15 (Minitab, Inc., State College, PA). A second-order
polynomial equation was applied to correlate the factors, Equation 4-1.
2

2

2

2

2

Y = a0 + a1X1 + a2X2 + a3X3 + a4X4 + a5X5 + a11X1 + a22X2 + a33X3 + a44X4 + a55X5 +
a12X1X2 + a13X1X3 + a14X1X4 + a15X1X5 + a23X2X3 + a24X2X4 + a25X2X5 + a34X3X4 +
a35X3X5 + a45X4X5

(4-1)

Where Y represents the electrotextile resistance in ohms and a0 is the intercept coefficient. The
values a1, a2, a3, a4, and a5 represent the coefficients of the linear terms. The values a11, a22,
a33, a44, a55, a12, a13, a14, a15, a23, a24, a25, a34, a35, and a45 represent the coefficients of the
quadratic terms. The values X1, X2, X3, X4, and X5 represent the variables of pyrrole
concentration, 3TAA concentration, FeCl3 concentration, 5SSA concentration, and reaction time.

80

Variables that were found to be highly correlated with other X variables were removed from the
equation. An ANOVA table and R-square of the model were obtained from analyses, which
were used to evaluate the model. Finally, the optimal conditions for synthesizing a low
resistance electrotextile were concluded from the model equation using MATLAB 7.12
(Mathworks, Inc., Natick, MA).

4.2.4 Analytical methods
Resistance measurements were taken across the fiber membranes using a four point probe and
Keithley 2400 Sourcemeter (Keithley Instruments, Cleveland, OH). A visual assessment was
conducted using scanning electron microscopy. The samples were gold sputter coated and
imaged with a Zeiss EVO 60 scanning electron microscope. (Carl Zeiss Microscopy, LLC,
Thornwood, NY).

4.3 Results and discussion
4.3.1. Mathematical model
Experimental data of electrotextile resistance is presented in Table 4-2. Supplementary data and
calculations can be found in Appendix B.2.

81

Table 4-2. Experimental results from central composite design.

Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Average
Resistance
( )
73.80
72.03
45.51
38.89
102.17
110.51
71.49
66.88
226.93
77.00
55.20
44.17
45.38
52.61
42.27

Run
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

Average
Resistance
( )
33.88
76.63
51.78
55.24
68.92
95.31
40.24
75.34
56.09
62.38
46.84
51.54
45.61
41.89
44.01

Runs were conducted in triplicate testing with 3 samples measured per testing, resulting in 9
measurements per run. These 9 measured resistance values were averaged together to obtain the
average resistance. Multiple regression analysis was performed to fit the response function with
the data. The resulting coefficients, standard errors, T-values, and P-values can be seen in Table
4-3.

82

Table 4-3. Results of regression analysis.

Predictor Coefficient
Constant
549.27

Standard
Error
65.14

T-value
8.43

P-value
<0.001

X1

-0.251

1.225

-0.21

0.838

X2

-4162

1114

-3.73

<0.001

X3

-3090.4

467

-6.62

<0.001

X4

-749.7

135.4

-5.54

<0.001

X5

-3.0857

0.5619

-5.49

<0.001

0.052653

0.009688

5.43

<0.001

X1X2

-77.773

7.799

-9.97

<0.001

X1X3

-10.074

3.899

-2.58

0.011

X1X4

-4.3741

0.9748

-4.49

<0.001

X1X5

-0.020077

0.004333

-4.63

<0.001

X2X3

33969

7019

4.84

<0.001

X2X4

8597

1755

4.90

<0.001

X2X5

31.353

7.799

4.02

<0.001

X3X4

2636.7

877.4

3.01

0.003

X3X5

16.41

3.899

4.21

<0.001

X4X5

5.4333

0.9748

5.57

<0.001

X1

2

The predictor values correspond to the variables in Equation 4-1. A P-value of 0.05 or less was
considered to be significant. X1 was kept in the equation despite having a P-value larger than
2

2

2

2

2

0.05 because of the significance of X1 . The variables of X2 , X3 , X4 , and X5 were found to
be highly correlated with other X variables and were removed from the equation. The resulting

83

equation, Equation 4-2, represents the electrotextile’s resistance as a function of pyrrole
concentration, 3TAA concentration, FeCl3 concentration, 5SSA concentration, and time.
2

Yresistance = 549.27 – 0.251X1 – 4162X2 – 3090.4X3 – 749.7X4 – 3.0857X5 + 0.052653X1 –
77.773X1X2 – 10.074X1X3 – 4.3741X1X4 – 0.020077X1X5 + 33969X2X3 + 8597X2X4
+31.353X2X5 + 2636.7X3X4 + 16.41X3X5 + 5.4333X4X5

(4-2)

Where Y represents the resistance of the electrotextile, X1 is the concentration of pyrrole, X2 is
the concentration of 3TAA, X3 is the concentration of FeCl3, X4 is the concentration of 5SSA,
and X5 is the reaction time.

An ANOVA table was used to evaluate the statistical significance of the model (Table 4-4).

Table 4-4. Analysis of variance (ANOVA) for the regression model from 5-factor central
composite design.
Sum of
Mean
squares
Degree of freedom
square
F-value P-value
Regression
315000
16
19688
28.42 <0.001
Residual Error
112922
163
693
Total
427922
179
R-square
0.736

The R-square of the model was 73.6%, which indicated that the observations fell reasonably well
on the fitted regression surface.

The response surfaces are shown in Figures 4-1 – 4-10 to demonstrate the effects of the factors
on the electrotextile resistance during synthesis.
84

70
Resistance
(Ohms)
50

30
0.1
5SSA
0.3
Concentration
(M)

0.5

50

60

70

80

90

100

Time (min)
Figure 4-1. The effects of reaction conditions on electrotextile resistance at 55% pyrrole,
0.025 g/mL 3TAA, and 0.1 M FeCl3.

85

80

Resistance
(Ohms)

60
40
20
0.06
0.1

FeCl

3

0.14

Concentration (M)

60

50

70

80

90

100

Time (min)

Figure 4-2. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole, 0.025
g/mL 3TAA, and 0.25 M 5SSA.

86

90
70
Resistance 50
(Ohms)
30
10
0.05
FeCl

0.15
3

Concentration (M)

0.25

0.2

0.1

0.3

5SSA Concentration (M)

Figure 4-3. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole,
0.025 g/mL 3TAA, and 75 minutes.

87

70
60
Resistance
(Ohms) 50
40
30
0.01
3TAA
0.03
Concentration
(g/mL)

50

60

70

80

90

100

Time (min)
Figure 4-4. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole,
0.1 M FeCl3, and 0.25 M 5SSA.

88

70
60
Resistance
(Ohms) 50
40
30
0.01
3TAA
Concentration 0.03
(g/mL)

0.2

0.1

0.3

5SSA Concentration (M)

Figure 4-5. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole,
0.1 M FeCl3, and 75 minutes.

89

90
70
Resistance
(Ohms) 50
30
10
0.05
FeCl Concentration
3

(M)

0.15 0.04

0.02

0.01

0.03
3TAA Concentration (g/mL)

Figure 4-6. The effect of reaction conditions on electrotextile resistance at 55 % pyrrole,
0.25 M 5SSA, and 75 minutes.

90

90
Resistance
(Ohms) 70
50
30
40
Time (min)

80
80

60

50

40

30

70
Pyrrole Concentration (% volume)

Figure 4-7. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL
3TAA, 0.1 M FeCl3, and 0.25 M 5SSA.

91

90
Resistance
(Ohms) 70
0.1
50
0.3
30
80

70

60

50
40
Pyrrole Concentration (% volume)

30 0.5

5SSA
Concentration
(M)

Figure 4-8. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL
3TAA, 0.1 M FeCl3, and 75 minutes.

92

100
Resistance
(Ohms)
60

20
0.08
0.1
FeCl Concentration
3

40
0.12
80

(M)

60
Pyrrole Concentration (%
volume)

Figure 4-9. The effect of reaction conditions on electrotextile resistance at 0.025 g/mL
3TAA, 0.25 M 5SSA, and 75 minutes.

93

100
Resistance
(Ohms)
60
20
0.01
0.02
3TAA Concentration
(g/mL)

40

0.03
0.04

80

70

30

50
60
Pyrrole Concentration
(% volume)

Figure 4-10. The effect of reaction conditions on electrotextile resistance at 0.1 M FeCl3,
0.25 M 5SSA, and 75 minutes.

Resistance was graphed against two of the modeled factors, while the other 3 factors were held
constant at the central composite center point. Electrotextile resistance decreased as the reaction
time and dopant concentration increased (Figure 4-1). Increasing the reaction time and the
oxidant concentration also reduced the electrotextile resistance (Figure 4-2). The combination of
increased oxidant and dopant concentrations decreased the electrotextile resistance (Figure 4-3).
Figure 4-4 shows that increasing the reaction time resulted in decreased resistance, regardless of
3TAA concentration, however at short reaction times the increase in 3TAA provided a slight
decrease in resistance. Likewise, increased dopant concentration also resulted in decreased
electrotextile resistance, regardless of 3TAA concentration (Figure 4-5). Figure 4-6 shows that
increases in oxidant concentration have a less significant effect on resistance when the 3TAA
94

concentration is higher and that a very low resistance can be achieved with a lower oxidant
concentration if the 3TAA concentration is high. In Figure 4-7 it can be seen that increasing
reaction time will decrease resistance, but that the lowest resistances can be found with a midrange pyrrole concentration. Approaching either the high or low extremes of the pyrrole
concentration will result in resistance increases. The trend of a mid-range pyrrole concentration
resulting in the optimum (lowest) possible electrotextile resistance can also be seen in Figures 48, 4-9, and 4-10. In Figure 4-8 changes in dopant concentration have nearly no effect at pyrrole
concentrations less than 45%. The highest resistances can be seen with high concentrations of
pyrrole and low concentrations of dopant. Increases in oxidant concentration can be seen to
reduce resistance, regardless of pyrrole concentration (Figure 4-9). Figure 4-10 shows that
increases in 3TAA concentration reduce electrotextile resistance at high pyrrole concentrations,
but will increase electrotextile resistances at low pyrrole concentrations.

4.3.2 Optimization
One of the most important tasks of the model was to find the optimal conditions in order to
produce the electrotextile with the lowest resistance for use in an electrochemical biosensor. The
optimization was conducted using the optimization toolbox in Matlab 7.12 based on the model
from Equation 4-2. The optimal values of each factor are listed in Table 4-5.

95

Table 4-5. Optimal conditions for minimum electrotextile resistance.

Factors
Pyrrole Concentration (% volume)
3TAA Concentration (g/mL)

Optimal
Conditions
58.79
0.03

FeCl3 Concentration (M)
5SSA Concentration (M)
Reaction Time (min)
Predicted Optimal Resistance ( )

0.11
0.25
70
35.41

Under the optimal conditions, the model predicted an electrotextile resistance of 35.41

.

4.3.3 Model Verification
Verification experiments were carried out to confirm the optimal conditions. Ten runs were
conducted under the optimal conditions, with each run’s generated sample measured in triplicate.
The results of the verification experiment can be seen in Table 4-6.

96

Table 4-6. Experimental results of optimal conditions.
Sample

Resistance
( )
30.30
39.63
31.40
50.60
42.80
35.13
33.00
39.10
38.20
39.87
38.00

1
2
3
4
5
6
7
8
9
10
Average
Standard
Deviation

6.01

The experimental data showed an average electrotextile resistance of 38.0

. The results were

analyzed using a Student’s t-test (1 tail, α = 0.025) and showed that the calculated t value of 1.36
was less than the tabulated t value of 2.26, therefore there was no significant difference and
demonstrated that the model fit the experimental data favorably. Thus, the optimal conditions
obtained from the central composite experimental design were valid. An SEM image of the
optimized electrotextile fibers can be seen in Figure 4-11.

97

Figure 4-11. SEM image of fibers made using optimal conditions for polymer coating
.
synthesis. Scale bar equal to 100 µm, magnification of 100x, EHT of 30 kV and working
magnification
distance of 6.0 mm.

4.4 Conclusions
There are many factors that contribute to the formation and resistance of conductive polymer
coatings. Five factors were identified in the synthesis of a conductive PPy – 3TAA co-polymer
co
used to create an electrotextile for the development of an electrochemical biosensor. They were:
electrochemical
concentration of pyrrole (monomer), concentration of 3TAA (monomer), concentration of FeCl3
(oxidant), concentration of 5SSA (dopant), and polymerization time. These factors were varied
in a central composite experimental design in order to develop a multiple regression model of the
design
polymerization reaction. This model was then used to find the optimal conditions for each factor
in order to generate the lowest experimentally possible electrotextile resistance. The optimal
conditions were: 58.79% volume of pyrrole, 0.03 g/mL of 3TAA, 0.11 M FeCl3, 0.25M 5SSA,
%

98

and 70 minutes of reaction time to generate an electrotextile resistance of 35.41

. These

conditions were experimentally verified and the mathematical model was statistically validated.
The optimization results can be used to aid in the production of a conductive electrotextile
electrode for electrochemical biosensors.

99

Chapter 5 : Antibody immobilization on conductive polymer coated nonwoven fibers for
biosensors

This chapter is adapted from our recently published work in Sensors and Transducers Journal:
McGraw, Shannon K.; Anderson, Michael J.; Alocilja, Evangelyn C.; Mareck, Patrick J.;
Senecal, Kris J.; and Senecal, Andre G. Antibody Immobilization on Conductive Polymer
Coated Nonwoven Fibers for Biosensors. Sensors and Transducers Journal. 2011. 13(12):142149. http://www.sensorsportal.com/HTML/DIGEST/P_SI_176.htm

5.1 Introduction
High-surface area nonwoven membranes are versatile and can be applied to biosensor
development [145]. One way to achieve this high surface area material is through the creation of
electrotextiles. Electrotextiles are fabrics that can function as conductive “wires” and physically
behave as textiles [160]. Electrotextile “smart membranes” can be designed for use with many
types of sensor signal transduction, however limited research has been conducted with the
integration of electrotextile, biological, and electrical technologies to create novel biosensor
systems for food safety [13]. Previous work has been done to develop electrically active nonmetallic textile coatings that are conductive using doped polypyrrole (PPy) polymer [14, 121,
122, 144, 166]. By producing a conformal conductive polymer coating on non-woven
microfibers, an electrochemical biosensor electrode can be created that is less expensive and has
more surface area for attachment than its planar metal counterparts [12]. In addition, these
electrotextile electrodes are durable, disposable, and have the potential for minimal required
attachment chemistry. With the attachment of biological recognition elements to the
100

electrotextile electrode surface, the electrodes would have the combined ability to perform
pathogen capture and detection [167, 168]. The creation of this type of electrode would simplify
a food pathogen detection biosensor, allowing the device to be smaller and lighter than current
systems. Small foot print biosensors with multiplexing capabilities, environmental robustness
and high sensitivity are needed for rapid presumptive testing. With membranes being designed
for the capture, the sample readers can be simplified and significantly reduced in size.

The objectives of this part of the study were: to determine the best immobilization method for
the attachment of antibodies to the electrotextile fibers, to determine the necessary concentration
of antibody to be used, and to determine what (if any) blocking agent should be used on the
antibody immobilized electrotextile fibers. In order to achieve these objectives, fluorescence
based measurements were taken using anti-Escherichia coli (E. coli) O157:H7 antibody labeled
with fluorescein isothiocyanate (FITC) and PicoGreen ® stained E. coli O157:H7 cells as optical
reporters. Three different immobilization methods were analyzed: passive adsorption,
glutaraldehyde attachment, and N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride
(EDC) / N-hydroxysulfosuccinimide (sulfo-NHS) cross-linking. These attachment methods were
each analyzed using 3 different antibody concentrations: 1, 10, and 100 µg / mL, as well as with
a negative control where no antibody was present. Finally, the use of either BSA or goat serum
as a blocking agent was compared. We have shown that antibodies can be successfully attached
to the electrotextile surface and still maintain the capability to capture their target pathogen,
making them viable for use in a biosensor system.

101

5.2 Materials and methods
5.2.1 Materials
Nonwoven polypropylene (PP) microfibers were obtained from North Carolina State Nonwovens
Cooperative Research Institute. For polymer synthesis, the monomer used was a pyrrole solution
that was copolymerized with carboxylic acid functional 3-thiopheneacetic acid (3TAA). This
co-polymer provides sites for covalent attachment of the bio-recognition (active) component of
the biosensor to the fiber based platform. The oxidant was iron (III) chloride (FeCl3). The
polymer was doped using 5-sulfosalicylic acid (5SSA). All polymerization chemicals were
obtained from Sigma-Aldrich (St. Louis, MO). The fiber characterization was conducted in
Chapter 3 [18]. Covalent attachment was performed using glutaraldehyde (Sigma-Aldrich) or
EDC (Sigma-Aldrich) and sulfo-NHS (Invitrogen, Carlsbad, CA, USA) with 50 mM 2-(Nmorpholino)ethanesulfonic acid (MES) buffer, pH 6.0, (Thermo Fisher Scientific, Waltham, MA,
USA). Blocking was conducted using 5% (w/v) bovine serum albumin (BSA) (Sigma-Aldrich)
or 5% normal goat serum (KPL, Gaithersburg, MD, USA). PicoGreen ® (Invitrogen) was used
for staining bacterial cells.

Two different antibodies against E. coli O157:H7 were used in this study, both polyclonal whole
IgG: (1) BacTrace antibody goat anti-E. coli O157:H7 (KPL) and (2) BacTrace antibody goat
anti-E. coli O157:H7 labeled with FITC (KPL). The antibodies were diluted in phosphate
buffered saline (1x PBS) to a concentration of either 1, 10, or 100 µg / mL immediately before
use.

102

5.2.2 Electrotextile synthesis
Electrotextile synthesis was conducted according to previously published techniques [18-20].
Briefly, an aqueous deposition process of conductive and functional polymer coatings upon a PP
fiber matrix was used, as described below. A 6 cm x 8 cm polypropylene microfiber mat was
submerged in a 10% pyrrole and 3TAA (10 mg/mL) solution, creating a functionalized monomer
that was absorbed onto the fiber mat. The wet fiber sample was then removed from the solution
and laid flat in a glass reaction vessel for polymerization. FeCl3 (0.1 M, 30 mL) was added to
the sample to initiate the chemical reaction while a dopant, 5SSA (0.1 M, 3 mL), was
simultaneously added. The fibers in solution were incubated at room temperature for 30 minutes
with constant agitation, thereby ensuring that polymerization occurred on both sides of the mat.
The nonwoven fiber sample was removed from the solution, gently washed on both sides with
deionized (DI) water, and dried at room temperature overnight. The fibers were then cut into
6.35 mm diameter circular discs.

5.2.3 Cell culture preparation
Cultures of E. coli O157:H7 Sakai strain (the strain isolated from the Sakai outbreak, 1996 in
Sakai City, Osaka, Japan [169]) and Salmonella enterica enterica, serovar Enteritidis (S.
Enteritidis) were obtained from the Michigan State University Food Safety and Toxicology
Center. E. coli O157:H7 cell cultures were grown for 4 hours in tryptic soy broth (TSB) at 37°C.
The growth time was selected based on a strain specific growth curve to give a concentration in
8

excess of 10 colony forming units (CFU)/mL [170]. S. Enteritidis cultures were grown
overnight in lactose broth at 37˚ C. A volume of 1 mL of cell culture was removed and pelleted
103

for 3 minutes at 5,000 x g. The pelleted cells were resuspended with 5 µL of PicoGreen and 500
µL of water. The cells were vortexed and stained for 5 minutes at room temperature while
rotating in order for the dye to cross-link with the DNA of the cells. The cells were then washed,
pelleted, and resuspended to a volume of 10 mL. PicoGreen is a fluorescent nucleic acid stain
used for quantitating double-stranded DNA (dsDNA). Any excess PicoGreen stain that was not
removed should have no effect due to the fact that it is non-fluorescent when unbound [171].

5.2.4 Antibody immobilization
Each immobilization and reporter combination was performed in triplicate, with the negative
control receiving no immobilization or staining chemistry. Each immobilization method was
performed with a series of 0 µg / mL, 1 µg / mL, 10 µg / mL, and 100 µg / ml of antibody. Each
series was done twice: with FITC-labeled antibodies immobilized on them and blocked with a
5% BSA solution and with PicoGreen stained E. coli O157:H7 cells with a 5% BSA block. Each
of the stains was also applied to samples without any antibodies immobilized to determine the
background noise from the stains and non-specific binding.

5.2.4.1 Passive adsorption
The conductive nonwoven fiber discs were washed with sterile distilled water and dried for 10
minutes. After drying, a volume of 250 µL of the desired antibody concentration was applied to
each disc and allowed to incubate for 1 hour with gentle agitation. After incubation, the discs
were washed twice with PBS [157].

104

5.2.4.2 Glutaraldehyde attachment
The conductive nonwoven fiber discs were washed with distilled water and dried for 10 minutes.
After drying, a volume of 25 µL of 2.5 mM glutaraldehyde was applied to each disc and
incubated for 1 hour at 4°C. After incubation, the discs were washed with distilled water and
dried for 10 minutes. A volume of 250 µL of antibody was then applied to each disc and
incubated for 15 minutes at 37°C. The discs were washed with distilled water, dried for 10
minutes, and then 50 µL of deactivating buffer (0.2 M Tris, 10 mM sodium cyanoborohydride)
[157] was applied and allowed to react at 37°C for 15 minutes. The discs were washed twice
with PBS [172].

5.2.4.3 EDC / sulfo-NHS cross-linking
The conductive nonwoven fiber discs were washed with distilled water, dried for 10 minutes, and
treated with 200 µL of EDC and sulfo-NHS in MES buffer with gentle agitation for 15 minutes.
The discs were then washed twice with MES buffer and a volume of 250 µL of antibody was
added and reacted with gentle agitation for 4 hours at room temperature. After incubation, the
discs were washed with MES buffer and then twice in PBS [157].

5.2.5 Blocking of the fibers
A volume of 50 µL of the blocking agent was added to the electrotextile fibers and allowed to
react for 1 hour. The samples were then washed twice with PBS and once with PBS containing
0.1 % Tween-20 (PBS-T).

105

5.2.6 Antibody immobilization analysis
The electrotextile fibers were separated into groups based on antibody attachment method:
passive adsorption, glutaraldehyde, or EDC / sulfo-NHS. Each group was split in half, with
FITC labeled antibodies being immobilized on half and non-FITC labeled antibodies being
immobilized on the other half. The antibody concentration was varied with concentrations of 0,
1, 10, and 100 µg/mL used. The fibers were then blocked with 5% BSA using the method
described in Section 5.2.5. The fibers that had non-FITC labeled antibody immobilized to the
7

surface received a volume of 200 µL of an approximate cell count of 1 x 10 CFU/mL of E. coli
O157:H7 cells stained with PicoGreen applied to them. The discs were incubated with the cells
at 37°C for 15 minutes and then washed three times with PBS. All experiments were performed
in triplicate.

5.2.7 Blocking agent analysis
BacTrace goat-anti E. coli O157:H7 antibodies were immobilized onto the electrotextile surface
at a concentration of 10 µg/mL using the glutaraldehyde attachment method described in section
5.2.4.2. Samples were prepared using no blocking agent (PBS as a negative control), 5% (w/v)
BSA, or 5% goat serum. Each sample was tested in triplicate. Bacterial cells of E. coli O157:H7
and S. Enteritidis were grown and prepared as described in section 5.2.3. Each blocking agent
was tested using 1 of 3 target samples: E. coli O157:H7 (positive control), S. Enteritidis
(negative control), or PBS containing no cells (system background noise) in order to determine
the effect of the blocking agents on non-specific binding onto the fibers. The target samples
7

were added in a volume of 200 µL to the discs at an assumed concentration of 1 x 10 CFU/mL.
106

The fibers were incubated with the target samples at 37˚C for 15 minutes and then washed with
PBS. The number of washes were varied, with either 0, 1, or 3 washes performed in order to see
if there was a change in non-specific attachment as more washes were performed.

5.2.8 Analytical methods
Fluorescence of the samples was measured using the Victor3 multilabel counter (PerkinElmer)
using a 490 +10 nm excitation bandpass filter and a 535 +25 nm emission bandpass filter. The
samples were analyzed and photographed using a fluorescent microscope (Olympus BX41 with
Q Color 3 camera and a QBC ParaLens Advance kit with blue excitation). All fluorescent
samples were excited at a wavelength of 490 nm and emission was measured at a wavelength of
535 nm.

5.3 Results and discussion
For this study, antibody-antigen binding assays were performed to determine the capture
capability of the antibody-functionalized conductive membranes. Attachment and capture
efficiency were determined through the use of fluorescent labels that were measured using a
fluorometer and visualized with confocal microscopy. Two different fluorescent indicators were
evaluated: FITC labeled to the antibody and PicoGreen stained E. coli O157:H7 Sakai cells. E.
coli O157:H7 Sakai was selected for use as the positive control based on its positive result when
tested against the KPL antibody using a Western blot. Three different antibody immobilization
techniques were evaluated: passive adsorption, covalent binding using glutaraldehyde, and EDC
/ sulfo-NHS cross-linking. For these experiments, three different antibody concentrations (1, 10,
and 100 µg / mL) were evaluated against a control with no antibody exposure. The effect of the
107

inclusion of 5% (w / v) bovine serum albumin (BSA) or 5% goat serum as blocking agents was
also evaluated. Supplementary data, calculations, and images can be found in Appendix B.3.

5.3.1 Selecting a fluorescent indicator
At an emission of 535 nm, the average fluorescence of the non-treated fibers was 35.3 relative
fluorescence units (RFU). The fluorescence intensity for each attachment method at antibody
concentrations of 0, 1, 10, and 100 µg / mL using FITC and PicoGreen stained E. coli O157:H7

Average Fluorescent Signal (RFU)

cells as indicators is shown in Figure 5-1.

1000
900
800
700
600
500
400
300
200
100
0

0 µg/mL
1 µg/mL
10 µg/mL
100 µg/mL

A,F
A,P
G,F
G,P
E,F
E,P
Attachment-Reporter Combinations

Figure 5-1. Average fluorescence due to attachment-reporter combinations when blocked
at varying antibody concentrations. Where ‘A’ stands for passive adsorption, ‘G’ for
glutaraldehyde attachment, ‘E’ for EDC attachment, ‘F’ for FITC labeling, and ‘P’ for
PicoGreen stain. Error bars show the range of the signal for each data set.

108

The lowest average fluorescence across all four concentrations was seen with glutaraldehyde
bound antibodies labeled with FITC. The highest average fluorescent signal was seen with
glutaraldehyde bound antibodies exposed to PicoGreen stained cells. In both passive adsorption
and glutaraldehyde binding, the PicoGreen stained cells outperformed their FITC labeled
counterparts at every concentration. At an antibody concentration of 100 µg/mL, the passively
adsorbed antibodies bound to PicoGreen stained cells emitted an intensity 188% higher than the
passively adsorbed antibodies labeled with FITC. The glutaraldehyde immobilized antibody
with the PicoGreen stained cells had an average RFU signal 535% higher than those with FITC
at 100 µg/mL. The higher fluorescent output of the PicoGreen stained cells can be due to the
fact that the PicoGreen stain causes the entire cell to fluoresce thus providing more signal than
the FITC labeled antibody which is significantly smaller in area. PicoGreen stained E. coli
O157:H7 cells were selected as the best fluorescent indicator of binding when compared to the
FITC labeling because of a higher RFU signal and its ability to indicate if bound antibodies were
present and had retained their capture ability.

5.3.2 Selecting an attachment method and antibody concentration
A comparison between the three different immobilization methods at three different antibody
concentrations with blocking using 5% BSA can be seen in Figure 5-2.

109

Average Fluorescent Signal (RFU)

1000
900
800
700
600

0 µg/mL

500

1 µg/mL

400

10 µg/mL

300

100 µg/mL

200
100
0
A

G
Attachment Methods

E

Figure 5-2. Average fluorescence for attachment methods at varying antibody
concentrations with blocking and PicoGreen reporter. Where ‘A’ stands for passive
adsorption, ‘G’ for glutaraldehyde attachment, and ‘E’ for EDC attachment. Error bars
show the range of signal for each data set.
PicoGreen stained E. coli O157:H7 cells were used as the fluorescent indicator. The highest
overall output was seen when using glutaraldehyde for covalent attachment, the lowest when
using EDC and sulfo-NHS. This trend was also previously seen in Figure 5-1. The relatively
small difference of 9.3 RFUs between the output values for the EDC immobilization at
concentrations of 10 and 100 µg/mL appears to indicate that all available binding sites for EDC
attachment are occupied with an antibody concentration of 10 µg/ml. The excess antibody
available at 100 µg/mL was unable to bind and washed off during rinsing. The results show that
all three methods tested were effective for pathogen capture to the fiber surfaces at antibody
concentrations of 10 or 100 µg/mL. At 1 µg/mL of antibody, there is no significant difference
from where no antibody is present. The differences in binding and fluorescent intensity is also
seen in Figure 5-3.
110

A

B

C

Figure 5-3. Fluorescent images of fibers at 200x with each attachment method, blocked, at
an antibody concentration of 100 µg/mL, with PicoGreen reporter. A: passive adsorption.
B: glutaraldehyde attachment. C: EDC / sulfo-NHS attachment.
The results in Figures 5-1 and 5-2 show that EDC and sulfo-NHS cross-linking have a lower
fluorescence than passive adsorption or glutaraldehyde. While some variability is expected due
to non-specific binding, it is probable that while the EDC and sulfo-NHS is binding the
antibodies to the surface, the EDC may not be accessible to bind the antibody due to steric
hindrance of the antibody and the surface. EDC is a zero length cross-linker while
glutaraldehyde has a six carbon spacer arm that allows better accessibility on a solid surface.
Also, since the antibodies are covalently attached directly to the carboxyl groups on the
membrane by the EDC and sulfo-NHS method there may be less antibody immobilized on the
membrane compared to the other methods due to a lack of potential binding sites. There is also
the potential for the orientation of the immobilized antibodies on the membrane surface to not be
optimal for analyte capture. Taking this into consideration, the method for antibody
immobilization on the conductive polymer coated nonwoven fibers that shows the most promise
is covalent binding using glutaraldehyde. When the need to produce the most cost effective
sensor is considered, it is determined that an antibody concentration of 10 µg/ml is required to
see a fluorescent change from samples with no antibody present.

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5.3.3 Blocking agent selection
In order to determine the effects of the use of a blocking agent, such as BSA or goat serum,
samples treated with each of the two blocking agents were compared to one another as well as to
samples that had no blocking agent treatment. Figure 5-4 shows the average RFU output for
samples with either no blocking agent added, a BSA block added, or a goat serum block added.
These blocks were tested against target samples that contained either no cells, E. coli O157:H7,

Average Fluorescent Signal
(RFU)

or S. Enteritidis.

600
500

no block

400
BSA
block

300
200

goat
serum
block

100
0
no cells

E.coli
S. Enteritidis
O157:H7
Applied Target Sample

Figure 5-4. Average fluorescence for electrotextile fibers with 10 µg/mL of antibody
attached using glutaraldehyde and varying blocking methods with either no cells added,
PicoGreen stained E. coli O157:H7 added, or PicoGreen stained S. Enteritidis cells added
and 1 wash performed. Error bars show the range of signal of each data set.
As can be seen in Figure 5-4, there is very minimal background noise (signal generated when no
cells are added) for all of the blocking methods, with all three reporting an average output of less
than 20 RFUs. All of the samples that had E. coli O157:H7 applied generated larger signals than
the other samples, this was expected since the antibody target for the fibers was E. coli O157:H7.
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The average signal when E. coli O157:H7 cells are applied ranges from 183.33 RFUs (BSA
block) to 412.67 RFUs (no block). The largest signal was expected to be found at the E. coli
sample with no blocking because E. coli O157:H7 is the target pathogen and should bind to the
antibodies, with no blocking being performed it is expected that non-specific binding to the
fibers is also occurring, adding to the signal. Of the samples that were exposed to S. Enteritidis,
the non-specific pathogen, the highest RFU output was from the unblocked sample with an
average value of 101 RFUs. The RFU signal from the non-specific binding of the S. Enteritidis
is much smaller than any of the signals resulting from the binding of the target E. coli indicating
that blocking reduces non-specific binding on the fibers.

Figures 5-5 and 5-6 show how the fluorescent output of the E. coli O157:H7 and S. Enteritidis

Average Fluorescent Signal (RFU)

exposed samples changed for each blocking method as more washes were applied, respectively.
600
500
400
300

0 washes
1 wash

200

3 washes

100
0
no block

BSA block goat serum block
Blocking Treatment

Figure 5-5. Average fluorescence for electrotextile fibers functionalized with anti-E. coli
O157:H7 antibodies with different blocking treatments over multiple washes with
PicoGreen stained E. coli O157:H7 cells as the fluorescent reporter. Error bars shows the
range of signal of each data set.
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The highest signal is seen from the sample with no blocking, but after 1 wash the signal,
increased. An increase in signal after 1 wash can also be seen in the sample blocked with goat
serum. The lowest signal comes from the sample blocked using goat serum, but with 0 washes.
At 0 washes all of the outputs signals are within 71 RFUs of each other. After 3 washes both of
the blocked samples have higher values than the sample that was not blocked. This shows that
the addition of a blocking agent to the fibers does not hinder the binding between the target and
antibodies. The variability between replicates of each data set was analyzed. The average
variability between points when no blocking was used was found to be 215.67 RFUs. The
average variability between data sets when the fibers were blocked with BSA or goat serum were
found to be 72.33 RFUs and 140.67 RFUs, respectively. The addition of the blocking agent
reduces the variance within samples. A possible explanation for the variability in each treatment
set as washes are added is that the antibody and target bacteria are not strongly bound. The
additional wash steps are then not just removing non-specifically bound cells, but also cells that
were captured to the target antibody.

Figure 5-6, the fluorescent output of the S. Enteritidis exposed samples for each blocking method
as more washes were applied, provides a better understanding of the effect of each blocking
method on non-specific binding.

114

Average Fluorescent Signal (RFU)

1200
1000
800
600

0 washes
1 wash

400

3 washes

200
0
no block

BSA block

goat serum
block

Blocking Treatment
Figure 5-6. Average fluorescence for electrotextile fibers functionalized with anti-E. coli
O157:H7 antibodies with different blocking treatments over multiple washes with
PicoGreen stained S. Enteritidis cells as the fluorescent reporter. Error bars show the
range of signal of each data set.

Figure 5-6 shows that all three blocking treatments reduced significantly after one wash,
implying that there was not a high amount of non-specific binding occurring on the fibers
regardless of blocking method if at least 1 wash step was used. The average RFU output of the
samples blocked using goat serum with 0 washes had a value of 286 RFUs. A value 173.33
RFUs less than the samples that had no blocking treatment and 330 RFUs less than the samples
blocked with BSA. After each wash step the samples blocked with goat serum had the lowest
average RFU output of all of the blocking treatments, ending with an output of only 34 RFUs
after 3 washes as compared to 52 RFUs for the samples with no blocking treatment and 100.67
RFUs for the samples blocked using BSA. By looking at the range of the data points within each
sample set it can also be seen that the addition of wash steps reduced variability. The addition of
a single wash step reduced the range in data points for samples with no blocking from 451 RFUs

115

to 49 RFUs. With BSA as the blocking agent the range went from 702 RFUs to 41 RFUs and
with goat serum blocking 56 RFUs to 26 RFUs.

Based on these results it was found that it is necessary to block and wash the electrotextile fibers
in order to prevent or remove non-specific binding. BSA and goat serum were both shown to
block non-specific binding on the fibers while not inhibiting target binding, however the goat
serum performs better because it results in lower initial non-specific binding as well as lower
variability in samples. The use of 1 wash step instead of 3 was also found to be beneficial. The
use of 1 wash reduced the non-specific binding on the fibers significantly and also did not appear
to reduce signal when the target pathogen was applied to the fibers. Additional wash steps may
remove captured target bacteria that are weakly bound to the immobilized antibodies.

5.4 Conclusions
The initial results from this study show that antibodies can be attached to conductive polymer
coated non-woven fibers. Passive adsorption, covalent binding using glutaraldehyde, or EDC
and sulfo-NHS are all effective immobilization methods, with glutaraldehyde demonstrating the
best antibody attachment. These results indicate that the best nonwoven sensor construction will
utilize glutaraldehyde attachment of 10 µg/mL of antibody with BSA blocking. This is
confirmed by challenging the sensor with PicoGreen stained cells and fluorescent detection.
Non-specific binding does occur on the fiber surfaces, however the addition of a blocking agent,
such as goat serum, and a wash step can reduce the effects of non-specific binding, aiding in the
specificity of the sensor. This technology was used in the capture and detection of live E. coli
O157:H7 cells in Chapter 6.
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Chapter 6 : A resistance based biosensor that utilizes conductive microfibers for microbial
pathogen detection

This chapter is adapted from our recently published work in Open Journal of Applied Biosensor:
McGraw, Shannon K.; Alocilja, Evangelyn; Senecal, Kris and Senecal, Andre. A Resistance
Based Biosensor That Utilizes Conductive Microfibers for Microbial Pathogen Detection. Open
Journal of Applied Biosensor. 2012. 1(3):36-43. DOI: 10.4236/ojab.2012.13005.

6.1 Introduction
Although food and waterborne pathogens do not have as significant an effect on U.S. military
operations as they have in the past, enteric pathogens are still one of the top causes of noncombat related injuries in the field and are therefore one of the primary military medical
concerns for deployed troops [1]. Gastroenteritis was the leading cause of illness among troops
during Operations Desert Shield (1990 – 1991) and Desert Storm (1991) [2]. This is a
significant issue for the military to address because an outbreak of diarrheal disease in the field
has the ability to rapidly affect a large number of warfighters. During Operation Restore Hope
(1992 - 1993), it was shown that 16 % of all hospital admissions were for diarrheal illness [3].
Of these admissions, 16 % could be traced back to Escherichia coli (E. coli). Various studies on
diarrheal illness in deployed troops have listed an array of enteric pathogens as the source with
the most commonly occurring ones being: Shigella, E. coli, Salmonella, and Campylobacter
species [2-4, 173]. The ability to rapidly and accurately detect enteric pathogens with low
infective doses, such as E. coli O157:H7, in the field has significant importance.

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Biosensors are one technology being developed in order to improve pathogen detection and
reduce response times. Immunobiosensors utilize pathogen specific antibodies coupled to a
transducer as the biological recognition element for detection [75]. The use of antibodies in the
design of a biosensor is beneficial because, as the field of clinical immunoassays has shown, the
benefits of the antibody-antigen reaction include high binding efficiency and specificity for
detection. In addition, antibodies can be generated against nearly any bacterial pathogen. The
faster speed and lower cost of immunobiosensors versus standard detection methods and
biosensors using other biorecognition techniques, such as DNA, have made them especially
marketable for use in food matrices [149-151]. In an electrochemical biosensor, the biological
recognition element is immobilized on an electrode, which then converts the biological
recognition event (i.e. antibody – antigen binding) into a measurable electrical signal [75].
Electrochemical biosensors are generally less expensive than optical detection methods and are
easier to use with turbid samples. Electrochemical impedance / resistance based sensing also
does not require enzyme labels or redox mediators to facilitate detection the way optical based
sensing does [127]. Instead, a measurable system response is created when the biological
recognition event disrupts the flow of either the current or the potential at the working electrode
while the reference electrode maintains a constant potential [75]. In order for biosensor
technology to advance and surpass common pathogen detection techniques, such as polymerase
chain reaction (PCR) and culture / colony counting, the current drawbacks of biosensors must be
addressed. These include high cost, low durability, a lack of environmental robustness for in
field testing, detection limits that do not reach those of traditional techniques, and a necessity for
extra extraction methods to be performed before use which adds to the total detection time.

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One approach for addressing some of the drawbacks of current biosensor technology is through
the development / use of nonwoven fibers to create “electrotextiles”. High-surface area
nonwoven electrotextile membranes are versatile materials that can be developed into “smart
membranes” designed for use with all forms of sensor signal transduction. Research is scarce,
however, regarding the integration of electrotextile, biological, and electrical technology to
create novel biosensor systems for applications such as food protection and environmental
sampling. Previous work has been done to develop electrically active non-metallic textile
coatings made of doped polypyrrole (PPy) polymer [14, 121, 122, 144]. By producing a
conductive polymer coating on nonwoven microfibers, an electrochemical biosensor electrode
can be created that is less expensive than its planar metal counterpart [12]. The overlapping fiber
layers also have more available surface area than planar electrodes, resulting in more potential
target attachment sites. In addition, these electrotextile electrodes can be engineered to be
durable, disposable, lightweight, and require minimal attachment chemistry. These qualities
make them ideal for in field use. The chemical composition of nonwoven fibers and their
coatings can also be easily changed or adjusted based on their intended use. Small changes to
the processing parameters can change the fiber diameter, mesh size, porosity, texture, or weave
pattern. This processing flexibility makes them a very versatile material for sensor development.
They can be designed to be used with many different analytes and experimental conditions and
can be designed to have high chemical stability [85]. The ability to use antibody functionalized
fibers for the capture and concentration of target analytes has been previously demonstrated with
electrospun nanofibers and a carboxyl functional group (-COOH) [19, 145]. With the attachment
of biological recognition elements to the electrotextile surface, these electrodes have the capacity

119

to perform pathogen capture, concentration, and detection. This would simplify a food pathogen
biosensor, resulting in a significantly smaller and lighter detection system.

A nonwoven polypropylene (PP) electrotextile coated with a pyrrole and 3-thiopheneacetic acid
(3TAA) conductive copolymer has been developed (Chapter 3) [18]. Studies were conducted to
look at the effects of the inclusion of different monomers, reaction solvents, and a dopant.
Analysis was also done to determine the best concentration of oxidant and functionalizing
monomer to use. The successful attachment of antibodies to the coated fibers for the capture of
bacterial cells using this electrotextile has been previously reported [19]. Bhattacharyya et al.
have shown that a nylon electrotextile coated with a 3,4-ethylenedioxythiophene (EDOT) and 3thiopheneethanol (3-TE) copolymer functionalized with avidin can be used to detect biotin in
solution [13]. They observed that the textile resistance increased as the concentration of biotin
protein in the solution increased and that the resistance of each sample also increased over time.
These increases in resistance were theorized to be the result of the surface bound avidin on the
electrotextile reacting with the biotin in solution and that the attachment of the biotin to the
surface altered the electrical environment close to the electrode layer. This same theory can be
applied to our sensor, the average resistance for each bacterial sample increases as the bacterial
cells impede the flow of electricity through the electrode. This response becomes larger and
more significant as the concentration of cells in solution increases.

There were three objectives to this study in order to establish a proof of concept for our sensor:
first, to determine the resistance of the electrotextile membrane in the electrochemical cell;
second, to determine what effect the rest of the system has on the total resistance and if that

120

effect is significant; and third, to determine if the electrotextile electrode can differentiate
between small changes in the conductivity of a solution due to the addition of target bacterial
pathogens. In order to achieve these objectives, experiments were conducted using serial
dilutions of E. coli O157:H7. First, resistances were measured in the system with and without
the conductive electrotextile in order to determine the baseline resistances for the system
components and how much they contribute to the total measured sensor resistance. Next,
multiple measurements were conducted over a large range of bacterial concentrations and
evaluated to determine if the detected change at each concentration was significantly different
from the blank (no bacteria) values. We have shown that a rapid, novel electrochemical
biosensor based on the use of polypropylene microfiber membranes coated with a conductive
polypyrrole and antibody functionalized can be used for the detection of E. coli O157:H7. To
our knowledge, this is the first time a functionalized conductive copolymer coated nonwoven
electrotextile has been used with immobilized antibodies as an electrode for the successful
electrical detection of bacterial cells.

6.2 Materials and methods
6.2.1 Materials
Nonwoven PP microfibers were obtained from North Carolina State Nonwovens Cooperative
Research Institute. For the polymer synthesis, the monomer used was a 10% pyrrole solution
that was copolymerized with carboxylic acid functional 3TAA. The oxidant was iron (III)
chloride (FeCl3). The polymer was doped using 5-sulfosalicylic acid (5SSA). All of the
polymerization chemicals were obtained from Sigma-Aldrich (St. Louis, MO). Covalent

121

attachment of KPL (Gaithersburg, MD) BacTrace antibody goat anti-E. coli O157 was
performed using glutaraldehyde (Sigma-Aldrich) and a deactivating buffer (0.2 M Tris, 10 mM
sodium cyanoborohydride (Sigma-Aldrich)) and blocked with 5% (w/v) bovine serum albumin
(BSA) (Sigma-Aldrich).

6.2.2 Electrotextile synthesis
Electrotextile synthesis was conducted according to previously published techniques [19].
Briefly, an aqueous deposition process of conductive and functional polymer coatings upon a PP
fiber matrix was used, as described below. A 6 cm x 8 cm PP microfiber mat was submerged in
a 10% pyrrole and 3TAA (10 mg/mL) solution, creating a functionalized monomer that was
absorbed onto the fiber mat. The wet fiber sample was then removed from the solution and laid
flat in a glass reaction vessel for polymerization. FeCl3 (0.1 M, 30 mL) was added to the sample
to initiate the chemical reaction while a dopant, 5SSA (0.1 M, 3 mL), was simultaneously added.
The fibers in solution were incubated at room temperature for 30 minutes with constant agitation,
thereby ensuring that polymerization occurred on both sides of the mat. The nonwoven fiber
sample was removed from the solution, gently washed on both sides with deionized (DI) water,
and dried at room temperature overnight.

6.2.3 Antibody Immobilization
Covalent attachment of the anti-E. coli O157 antibodies onto the electrotextile fiber surface was
conducted following a previously published technique [19]. Briefly, the conductive nonwoven
fiber mats were cut into 2 cm x 2 cm squares. The fiber squares were washed with 0.01 M
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phosphate-buffered saline (PBS), pH 7.4, and dried for 10 minutes. After drying, a volume of 3
mL of 2.5 mM glutaraldehyde was applied to each disc and incubated for 1 hour at 4°C. After
incubation, the discs were washed with 0.01 M PBS and dried for 10 minutes. A volume of 4
mL of antibody (10 µg/mL in PBS) was then applied to each disc and incubated for 15 minutes
at 37°C. The discs were washed with 0.01 M PBS, dried for 10 minutes, and then 3 mL of
deactivating buffer was applied and allowed to react at 37°C for 15 minutes. A 5% BSA block
was added and reacted at room temperature for 1 hour. The discs were washed twice with PBS,
and then once with PBS containing 0.1 % Tween-20 (PBST).

6.2.4 Cell culture preparation and enumeration
E. coli O157:H7 Sakai strain was obtained from the Michigan State University Food Safety and
Toxicology Center. It was selected as the target pathogen after testing positive against the KPL
antibody using a Western blot. Cell cultures were grown to a concentration of roughly 10

8

colony forming units (CFU)/mL based on a strain specific growth curve [170] in tryptic soy
broth (TSB) at 37°C. A 10 mL volume of cell culture was removed and centrifuged for 5
minutes at 5,000 rpm. The pelleted cells were washed with Butterfield’s phosphate buffer
(BPB), pH 7.2. This wash procedure was performed in triplicate. The cells were resuspended
(10 mL) and then serially diluted using BPB. A volume of 100 µL of the 5th, 6th, 7th, and 8th
dilutions as well as the blank control sample, sterile BPB, was plated in triplicate onto
MacConkey-sorbitol (SMAC) agar and incubated overnight at 37˚C. Sample concentration
estimates were calculated using standard estimation methods based on the average plate counts
[174, 175].

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6.2.5 Resistance measurements and electrical theory
Resistance values were obtained by connecting the electrotextile electrodes to a potentiostat with
two stainless steel alligator clip. The experimental setup can be seen in Figures 6-1and 6-2.

Computer potentiostat to
measure resistance

Conductive polymer coated polypropylene fibers

Figure 6-1. Schematic of the system design. The electrotextile fibers are connected
to the computer / potentiostat for resistance measurements.

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Alligator
clips
Conductive
polymer
electrotextile

Figure 6-2. Experimental set-up that measures the change in resistance of the antibody
bound electrotextile electrode when bacteria are added to the buffer solution. Alligator
clips connect the conductive polymer electrotextile to the computer / potentiostat where
the measured current of the system is recorded.
Briefly, the antibody bound 2 cm x 2 cm electrotextile squares were completely submerged in a
beaker containing 0.1 M phosphate buffer (18 mL, pH 7.4) while attached to the potentiostat in
order to establish a baseline resistance for the fibers. After 30 minutes, a 2 mL sample from the
E. coli O157:H7 serial dilutions was added, bringing the total solution volume to 20 mL.
Constant potential amperometry (0.5 V), where a constant potential is applied and the current is
measured, was used and the current values were recorded at fixed time intervals over 15 minutes.
Previous studies have demonstrated that a potential of 0.2 V to 0.8 V is a preferable input signal

125

when dealing with whole cells or other biological elements [176-179]. Based on this
information, the potential of 0.5 V was arbitrarily selected.

6.2.5.1 Screening for ohmic behavior
Because a direct current (DC) power source was used with a constant potential, the measured
currents at each time point could be used to calculate the total resistance of the system based on
Ohm’s law, Equation 6-1. Ohm’s law states that the current flowing through a conductor, at a
constant temperature, is directly proportional to the potential difference across the points of
applied voltage.
V=IR

(6-1)

Where V is the potential across, I is the current through, and R is the resistance of the system.
The ratio of V / I indicates the resistance value at that point in time. To validate the use of this
equation, a linear sweep was performed on the electrotextile. A linear sweep is a voltammetric
method where the applied potential to the electrode is linearly varied in time. A material that
produces a linear response to a linear sweep is described as an ohmic material, a material that
exhibits ohmic behavior

6.2.5.2 Circuit with two resistors in parallel
The complete system setup can be viewed as a simple circuit with two resistors in parallel, as can
be seen in Figure 6-3.

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Bacterial cell in
sample solution

R

2

R

1

Y Y Y Y Y Y

Immobilized
antibody layer

Electrotextile fibers
Figure 6-3. Circuit model of the antibody immobilized fibers with bacteria present. The
system acts as a circuit with two resistors in parallel. R1 is the resistance at the
electrotextile surface. R2 is the resistance in the buffer solution.
The resistance caused by interactions at the conductive fiber electrode surface would be
considered resistor 1 (R1) and any resistance occurring due to interactions between the buffer
solution and the rest of the system components would be considered resistor 2 (R2). Based on
the theory of parallel resistors, these two resistances combine to form the total resistance of the
system (RT) as seen in Equation 6-2.
1 ⁄ RT = 1 ⁄ R1 + 1 ⁄ R2

(6-2)

R2 can be determined by measuring the resistance of the system when the conductive fiber
electrode is not present. Once measured values for RT and R2 have been obtained, R1 can be
calculated. In order to determine if the effect of R2 is significant in the system, it is important to

127

look at how much it contributes to RT. Equation 6-3 was used to determine what percentage of
the total system resistance, RT, was due to R2.
R2 % contribution to RT = ∆R ⁄ RT × 100

(6-3)

Equation 6-3 calculates the contribution to RT from R2 as a percentage, where ∆R is equal to the
difference between RT and R1, or the difference in the total system resistance due to R2.

6.2.6 Determining system resistances
In order to address objective 1, squares (2 cm x 2 cm) of the electrotextile fabric were attached to
the potentiostat in phosphate buffer (PB) and their amperometric response to the addition of E.
0

3

5

8

coli O157:H7 samples at calculated concentrations of 10 , 10 , 10 , and 10 ( log 0, 3, 5, and 8,
respectively) CFU/mL as well as a control (BPB, 0 CFU / mL) at fixed time points over 15
minutes was recorded. Using Equation 6-1, RT was calculated.

6.2.7 Determining the system’s component contributions
In order to address objective 2, the procedure described in Section 6.2.6 was performed using 2
cm x 2 cm squares of non-coated, non-functionalized PP fibers. Using Equation 6-1, these
values were designated as R2. Using Equation 6-2, the value of R1 at each time point was
calculated. Once the values of R1, R2 and RT were established, Equation 6-3 was used to

128

determine the percent contribution to RT that was due to the resistance from R2. Significance
was tested by using a Student’s t-test (2 tail, α = 0.05).

6.2.8 Using the electrotextile as a resistance based sensor
To address objective 3, squares (2 cm x 2 cm) of the electrotextile fabric were attached to the
potentiostat in PB and multiple measurements were first taken using the electrotextile electrodes
and constant potential amperometry with a pure BPB solution over several days to establish the
range of the initial baseline values for the system. Triplicate measurements were taken using the
electrotextile electrode and constant potential amperometry to establish the resistance values for
1

4

6

a control sample (BPB, 0 CFU/mL) and E. coli O157:H7 at concentrations of 10 , 10 , 10 , and
9

10 CFU/mL. The resistances for each concentration at each time point were averaged to
determine an average resistance value for each concentration. The resistance of the sensor at
each bacterial concentration was tested against the values of the control using a Student’s t-test
(2 tail, α = 0.05) to determine significance. The measured biosensor resistances were normalized
using Equation 3-1 to determine the system response (Rp) [13].
Rp(%)=((R1-R0)/R0 )×100

(3-1)

Where R1 is the resistance of the biosensor after the positive bacteria sample has been added. R0
is the resistance of the biosensor that has not been exposed to bacteria (blank). This was done in
order to pool and analyze multiple experiments with one another to determine if the biosensor
results were reproducible.

129

6.3 Results and discussion
The purpose of this study was to determine: the resistance of the membrane in the
electrochemical cell; what effect the buffer solution has on the total resistance of the system and
if that effect is significant; and if the electrotextile electrode can differentiate between small
changes in the conductivity of a solution due to the addition of target bacterial pathogens. As a
result of this study a functionalized conductive copolymer coated nonwoven electrotextile was
successfully used as an electrode with immobilized antibodies on the surface for the successful
electrical detection of bacterial cells for the first time. This technology has the potential to be
used in the development of light-weight, flexible, inexpensive, and disposable field based
systems for the rapid detection of various target pathogens. Supplemental data and calculations
can be found in Appendix B.4.

6.3.1 Screening for ohmic behavior
Data showing ohmic behavior of the electrotextile is shown in Figure 6-4.

130

3000
2000
1000
0
-1000
-2000
-3000
-4000
-1.5

-1

-0.5

0

0.5

1

1.5

Figure 6-4. The average linear sweep of the electrotextile fibers over the potential range
of interest, -1.0 to 1.0 V. The vertical axis is measured current output in micro amps.
The solid line shows the linear best fit of the data points with an equation of y=2756.4x112.49 and has an R2 value of 0.9949.
The electrotextile exhibits ohmic behavior across the potential range of interest, meaning that it
obeys Ohm’s law, as the potential increases, the measured current increases proportionally. The
electrotextile can therefore be used as an electrode for this study.

6.3.2 Determining system resistances
Using the method described in Section 6.2.6, amperometric responses to the addition of E. coli
0

3

5

8

O157:H7 samples at calculated concentrations of 8.37 x 10 , 10 , 10 , and 10 CFU/mL as well
as a control (BPB, 0 CFU/mL) at fixed time points over 15 minutes were recorded. Using
Equation 6-1, RT was calculated, achieving objective 1. These results can be seen in Figure 6-5.

131

Resistance (Kohms)

12.0
Blank (0
CFU/mL)

10.0
8.0

8.37E+00
CFU/mL

6.0

8.37E+03
CFU/mL

4.0
2.0

8.37E+05
CFU/mL

0.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Time (minutes)

8.37E+08
CFU/mL

Figure 6-5. Total system resistance (RT) over 15 minutes at varied bacterial cell
concentrations. As the concentration of bacterial cells increases, so does the measured
system resistances.
The average total system resistance (RT) ranged from 5.8 to 7.3 k , with the resistance
increasing as the concentration of bacterial cells in solution increased. The cytoplasm of live
bacterial cells has been previously reported to have a resistivity of 10

6

2

/ cm , which explains

this phenomenon [180, 181]. The measurements appeared to settle at a near constant resistance
after roughly 2-3 minutes had passed and the system had reached equilibrium.

6.3.2 Determining the system’s component contributions
In order to complete objective 2 the method described in Section 6.2.7 was performed. The
difference between the methods described in Sections 6.2.6 and 6.2.7 is that in Section 6.2.7 2
cm x 2 cm squares of non-coated, non-functionalized PP fibers were used. Using Equation 6-1,

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these values were designated as R2. Using Equation 6-2, the value of R1 at each time point was
calculated. The results of this experiment can be seen in Figure 6-6.
250000

Resistance (Kohms)

200000

Blank (0
CFU/mL)

150000

8.37E+00
CFU/mL

100000

8.37E+03
CFU/mL

50000
8.37E+05
CFU/mL
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Time (minutes)

8.37E+08
CFU/mL

Figure 6-6. Resistance of the buffer solution (R2) without the electrotextile present over
15 minutes at varied bacterial cell concentrations.

The average resistance of the system without the coated fibers (R2) ranged from 10.5 M to 21.5
3

M , a 10 magnitude difference from the values of R1. The values of R2 for each cell
concentration exhibited the same general trend as seen with the values of RT, but were more
variable with random fluctuations occurring as late as 10 minutes into the measurement. As can
be seen in Figure 6-6, at 2 different points during the experiment the measured resistance peaked
to a value of roughly 200,000 k . This is most likely due to the fact that at such high resistance,
and therefore low current levels, the measured values were near the limit of sensitivity for the
potentiostat and difficult to separate from the regular system noise. The values of R2 also had
133

larger standard deviations, ranging from 7.8 to 37.8 M , as compared to 1.2 to 1.5 k

for the

values of RT.

With the values of R1, R2 and RT established, Equation 6-3 was used to determine that at its
maximum point, the contributed resistance from R2 was calculated to be 2.8% of the total system
resistance. After 2 minutes, R2 contributed at maximum only 0.73% to the total system
resistance, with the average percentage of RT due to R2 being 0.09%. A Student’s t-test (2 tail, α
= 0.05) was performed and found no significant difference between RT and R1 at any of the
tested concentrations. With R1 contributing over 97% of the value of RT, RT and R1 were
determined to be essentially equivalent. All future reported resistances and analysis for the
sensor are based on the value of RT.

6.3.3 Using the electrotextile as a resistance based sensor
Multiple measurements were taken using the electrotextile electrodes and constant potential
amperometry with a pure BPB solution to establish a baseline range for the system. Within the
experimental lot the sensor was found to have an initial resistance range of 5.8 k to 13.0 k
with an average value of 9.6 k

(standard deviation = 3.1 k ). This range can be attributed to

variability in the polymer synthesis and deposition process as well as the potential for
inconsistencies of the amount and orientations of the bound antibodies and blocking proteins
between the fiber membranes.

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Triplicate measurements were taken using the electrotextile electrode and constant potential
amperometry to establish the resistance values for a control sample (BPB, 0 CFU/mL) and E.
1

4

6

9

coli O157:H7 at concentrations of 10 , 10 , 10 , and 10 (log 1, 4, 6, and 9, respectively)
CFU/mL. A time of 3 minutes was determined to be necessary to reach system equilibrium. The
resistances for each concentration at each time point were averaged and the value after the initial
3 minute equilibrium time can be seen in Figure 6-7.
25.0

15.0

1.39E+01
CFU/mL

10.0

1.39E+04
CFU/mL

5.0

1.39E+06
CFU/mL

0.0

Average Resistance (Kohms)

20.0

Blank (0
CFU/mL)

1.39E+09
CFU/mL

3

4

5

6

7

8 9 10 11 12 13 14 15
Time (minutes)

Figure 6-7. Average resistances over a period of 12 minutes (after a 3 minute equilibrium
time) for the triplicate runs of the electrotextile biosensor with varying bacterial cell
concentrations, fibers from experiment A. The resistances increase as the concentration
of cells increases. A visible difference in response can be observed between all of the
samples and the control.
At each concentration, the change in the resistance values between 3 and 15 minutes was less
than 2k , the resulting relatively smooth stacked lines for each of the samples indicate that the
resistance of the sensor at each concentration is not significantly changing over the 12 minute
period. The resistance for each sample increases as the concentration of cells increases. This

135

trend can also be seen in Figure 6-8, where the individual resistance values at each time point
after 3 minutes for each sample concentration were averaged together to determine an average
resistance for each sample.

Average Resistance (Kohms)

35
30
25
20
15
10
5
0
Blank

1.39E+01
1.39E+04
1.39E+06
Concentration (CFU/mL)

1.39E+09

Figure 6-8. Average resistances for varying bacterial cell concentrations, fibers from
experiment A. Resistances taken after 3 minutes equilibrium time for each cell
concentration were averaged to provide a single resistance value. Error bars show the
standard deviations of the experimental data.
In Figure 6-8, it can be seen that the change in average resistance values is smaller between the
high concentrations than the changes observed between the lower half of the concentrations.
This is most likely due to the fibers reaching a threshold for attachment on the surface, so that
the increase of cells in the sample is no longer generating a proportional increase on the system
resistance. The surface attachment capabilities could be improved by decreasing the size of the
fibers, allowing more fibers per square centimeter and therefore increasing the surface area of the
fiber mats. It can also be improved by attaching more antibodies to the fibers and decreasing the
amount of blocking agent being used.
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The resistance of the sensor at each bacterial concentration was tested against the values of the
control using a Student’s t-test (2 tail, α = 0.05) to determine significance. The resistance at each
concentration of E. coli O157:H7 tested was determined to be significantly different from the
blank. The Student’s t-test was also used to determine that, after 3 minutes, the resistances
measured at each concentration were significantly different than at every other tested
4

6

concentration with two exceptions: between 1.39 x 10 CFU/mL and 1.39 x 10 CFU/mL and
6

9

between 1.39 x 10 CFU/mL and 1.39 x 10 CFU/mL. Within each individual run of the
triplicate, the electrotextile electrode was capable of differentiating between each of the samples,
with resistance at each concentration found to be significantly different than the resistance at
every other tested concentration. This shows that the electrotextile electrode is capable of
differentiating between a positive and negative sample, but cannot be used for quantitative
analysis. Due to manufacturing variations resulting in different initial resistances for the sensor
when no bacteria are present, it is important for each experiment to have a blank control run with
fibers from the same lot and for the data to be normalized in order to accurately determine
positive / negative results.

6.3.4 Sensor reproducibility
The experiment conducted in Section 6.3.3 was repeated 3 more times in order to test for
reproducibility of the results. Each individual run was tested in triplicate. In order to
compensate for the variability in the initial resistance values between sensors, the data was
normalized into a percent response using Equation 3-1. The responses for each dilution at each
time point across the triplicates were averaged together to provide an average performance of the

137

sensor for each experiment set. The results of these additional experiments can be seen in
Figures 6-9, 6-10, and 6-11.
45

Average Response (%)

40

9.10E+00
CFU/mL

35
30

9.10E+03
CFU/mL

25
20
15

9.10E+05
CFU/mL

10
5
0
-5

3

4

5

6

7

8 9 10 11 12 13 14 15
Time (minutes)

9.10E+08
CFU/mL

Figure 6-9. Average response over a period of 12 minutes (after a 3 minute equilibrium
time) for the electrotextile biosensor with varying bacterial cell concentrations, fibers
from experiment B.

138

Average Response (%)

100
90
80
70
60
50
40
30
20
10
0

7.30E+00
CFU/mL
7.30E+03
CFU/mL
7.30E+05
CFU/mL

3

4

5

6

7

8 9 10 11 12 13 14 15
Time (minutes)

7.30E+08
CFU/mL

Figure 6-10. Average response over a period of 12 minutes (after a 3 minute equilibrium
time) for the electrotextile biosensor with varying bacterial cell concentrations, fibers from
experiment C.

139

Average Response (%)

100
90
80
70
60
50
40
30
20
10
0

9.50E+00
CFU/mL
9.50E+03
CFU/mL
9.50E+05
CFU/mL

3

4

5

6

7 8 9 10 11 12 13 14 15
Time (minutes)

9.50E+08
CFU/mL

Figure 6-11. Average response over a period of 12 minutes (after a 3 minute equilibrium
time) for the electrotextile biosensor with varying bacterial cell concentrations, fibers
from experiment D.
When all 4 experimental sets conducted in triplicate were pooled together, the average resistance
of the blank study (no bacteria) had a wide variation of 10.9 k
value of 119.5 k

to 225.2 k

with an average

(standard deviation = 88.9 k ). This range can be attributed to variability in

the manufacturing processes (polymer synthesis and deposition), the potential for inconsistencies
of the amount and orientations of the bound antibodies and blocking proteins between the fiber
membranes, and variability in the biosensor connections between runs. Because the range in the
initial baseline for the system between lots is large, it is necessary to evaluate the system based
on the response of the sensor and the average observed trends across multiple sensors.
Normalizing the data using the signal to noise ratio to look at the sensor response can help
minimize the effect caused by that variability.

140

All of the experiments were shown to follow the same trend, horizontal lines vertically stacked
0

for each of the samples, with the samples with concentrations in the 10 CFU/mL range having
8

the lowest response and the samples with concentrations in the 10 CFU/mL range having the
highest response. The sensor response increases as the concentration of bacterial cells in the
samples increases. Using a Student’s t-test (2-tails, α = 0.05) showed the in each experiment the
sensor response for each positive concentration was significantly different than the blank sample.
Lots A, B, and C could not significantly differentiate between the samples with concentrations in
5

8

the range of 10 CFU/mL and 10 CFU/mL. Lot B could also not significantly differentiate
0

3

between the samples with concentrations of 9.10 x 10 CFU/mL and 9.10 x 10 CFU/mL.

A total of 18 runs over 8 experiments conducted over multiple days were pooled together. The
cumulative average response can be seen in Figure 6-12.

141

70
Average Response (%)

60
10^0 CFU/mL

50

10^1 CFU/mL

40

10^3 CFU/mL

30

10^4 CFU/mL
10^5 CFU/mL

20

10^6 CFU/mL

10

10^8 CFU/mL

0

10^9 CFU/mL
3

4

5

6

7 8 9 10 11 12 13 14 15
Time (minutes)

Figure 6-12. Cumulative average response at each time point of pooled multiple day
data over a period of 12 minutes (after a 3 minute equilibrium time) for the
electrotextile biosensor with varying bacterial cell concentrations.
In Figure 6-12, it can be seen that the original general trend of vertically stacked horizontal lines,
with the response increasing as the concentration increases is still observed. There is overlap,
however, between samples with close concentrations. Using a Student’s t-test (2 tails, α = 0.05)
each sample was found to be significantly different from the blank. The t-test also showed that
the sensor cannot significantly differentiate between positive samples, especially samples that are
0

1

8

9

close in concentration (e.g. 10 and 10 CFU/mL, 10 and 10 CFU/mL). Within each of the
experiments, a Student’s t-test showed that the response of the sensor at each of the tested
positive concentrations was significantly different than the blank. The lowest tested
0

concentration was 3.23 x 10 CFU/mL. The highest tested concentration was 1.39 x 10
CFU/mL.

142

9

Based on the data, the trends in the biosensor results were found to be reproducible, however due
to the variations between lots and the difficulty in differentiating between concentrations, the
biosensor is not yet capable of being used as a quantitative detection system. The biosensor does
work as a yes / no screening system. The results show that the biosensor has an experimental
0

lower limit of detection of 3.23 x 10 CFU/mL for the detection of E. coli O157:H7 in pure
culture.

6.4 Conclusions
A high surface area electrotextile based biosensor has been developed by the aqueous deposition
of a functionalized conductive copolymer onto a nonwoven polypropylene microfiber mat.
Pathogen specific antibodies were covalently attached to the fibers using glutaraldehyde. The
proof of concept for the system was established by measuring the resistance response of the
fibers when they were exposed to a target pathogen in solution over 15 minutes. By evaluating
the system as a circuit with two resistors in parallel, it was found that the resistance of the system
not associated with the electrotextile fibers contributed to only 2.8% of the total system
resistance, and was determined not to be a significant contribution. The biosensor was shown to
be able to differentiate between changes in conductivity due to the presence of a target pathogen
0

9

in a solution over a sensitivity range of 10 – 10 CFU/mL. By normalizing the resistance data
using the signal to noise ratio to calculate the biosensor response, the reproducibility of the
biosensor results was analyzed. It was found that the trends in the biosensor results were
reproducible, however due to the variations between lots and the difficulty in differentiating
between samples of different concentrations, the biosensor is not yet capable of being used as a

143

quantitative detection system. The ability of the biosensor to detect significant differences
between samples that do and do not contain bacteria, as shown using a Student’s t-test, indicates
that the biosensor can work as a yes / no screening system. The initial results show that the
0

biosensor has an experimental lower limit of detection of 3.23 x 10 CFU/mL for the detection
of E. coli O157:H7 in pure culture. This technology has the potential for application in the
development of a light-weight, flexible, inexpensive, and disposable field based system for the
rapid detection of various target pathogens.

144

Chapter 7 : Discussion and conclusions

The combined research presented in Chapters 3-6 describe the development of an
electrochemical biosensor based on the use of nonwoven microfiber membranes coated with a
conductive polymer and functionalized with antibodies for the biological capture and detection
of Escherichia coli O157:H7, one of the U.S. military’s top pathogens of interest for the
development of rapid diagnostic systems. E. coli O157:H7 can cause severe gastroenteritis and
is often spread through the consumption of contaminated food and water, which would rapidly
render a large number of warfighters ineffective in performing their duties. Current standard
detection technologies can be cumbersome and require generous quantities of chemicals and time
to operate. The current FDA gold standard for identification of this pathogen from food matrices
takes up to 3 days to generate a confirmed result. The research in this dissertation presents a
rapid, novel, and inexpensive approach to the detection of E. coli O157:H7. The developed
biosensor uses pathogen specific antibodies attached to conductive polymer coated nonwoven
fibers for identification in 15 minutes.

The fabrication of electrically conductive fabrics is not a new concept. Previously, this type of
material has been created by blending or mixing a conductive powder such as carbon black or
silver particles with a polymer melt that is then extruded to form fibers [182]. The disadvantage
of this method is that in order to achieve conductivity, the concentration of conductive powder
must be high enough that the particles touch each other in order to conduct electricity [121].
This can result in changes to the fibers’ physical properties that are unintended and undesired.
To address this Gregory, Kimbrell, and Kuhn have published on the creation of conductive
145

fabrics by polymerizing pyrrole onto the surface to textile fibers (e.g. cotton, quartz,
polyethylene terephthalate) [14, 16, 122, 166]. The most commonly used fiber material in these
works is nylon. Gregory, Kimbrell, and Kuhn all reported performing a slow (2 – 6 hours), low
concentration chemical oxidation onto the surface of nylon fabrics by combining the monomer
(pyrrole) and oxidant (FeCl3) and allowing them to react and form a pre-polymer species. This
pre-polymer was then epitaxially deposited onto the surface of the fibers where polymerization
was completed. The method detailed in this work for the fabrication of a conductive polymer
coated fabric differs from these works in three significant ways. First, although the previous
works cited did not report any material deformation or loss of strength when using nylon as the
fiber platform, this was observed during the work detailed in Chapter 3. In this work, a
nonwoven polypropylene fiber platform was coated in a conductive polypyrrole polymer coating
using FeCl3 as an oxidant, water as a solvent, and 5SSA as a dopant. The polypropylene fibers
were found to better maintain their original material strength and durability post-polymerization.
The previously published methods may not have suffered from this post-polymerization
deformation because the solutions used were at relatively dilute concentrations and the order of
addition to the fibers differed from this work. While the previously cited works formed a prepolymer from the monomer and oxidant that was then applied to the fibers and allowed to react
over several hours, in this work a two stage process was used where the monomer was adsorbed
into the fibers and then reacted with the oxidant. The change in addition order, higher
concentrations, and additional doping using 5SSA resulted of the fabrication of a black
polypyrrole coating (more heavily oxidized) on the fiber surfaces in far less time (30 minutes).
This change in the addition order of the reactants to the fibers helped improve the conformal
nature of the coating at the higher concentrations used. Additionally, a carboxyl functional
146

group was added to the polymer via the addition of 3TAA as a co-monomer with the pyrrole in
order to provide potential binding sites for biorecognition elements. The coated fibers were
found to be conductive while still maintaining surface area and fiber durability.

In order for the conductive fibers to be used in a resistance based biosensor, there must also be a
method available to bind biological molecules to the surface. One way of achieving this binding
is by building a functional group into the fiber. In Chapter 3, a carboxyl functional group was
added to the polymer by adding 3TAA as a co-monomer with the pyrrole. The binding of
biological molecules such as antibodies to surfaces functionalized with carboxyls is a wellestablished process [157]. The use of 3TAA as an individual monomer or as a co-monomer with
pyrrole to create conductive polymer thin films has been previously published [15, 183-185],
however the combination of 3TAA and pyrrole for the synthesis of a functionalized conducting
polymer thin film to be used for biological attachment has only been published once using vapor
deposition [17]. Our approach however is liquid adsorption onto the fibers. In Chapter 3, the
addition of a carboxyl functional group via 3TAA as a co-monomer with the pyrrole was
analyzed using three-dimensional polypropylene fibers as the platform surface for the aqueous
polymerization reaction. It was found to result in changes to the coating morphology, resistivity,
elemental sulfur presence, and available binding sites for biorecognition elements, with each
increasing as the concentration of 3TAA increased. This result was consistent with previously
published work that found that increasing the ratio of pyrrole to 3TAA resulted in higher
polymer conductivities [15, 17] but less binding potential for biological attachment [17].
Because increasing the concentration of 3TAA in the monomer solution resulted in increased

147

binding sites for biorecognition elements but also decreased the conductivity of the fibers, a
concentration of 10 mg/mL was initially selected for use in developing the biosensor.

Biological attachment using avidin-biotin was achieved on the fibers and assessed optically and
electrochemically. The use of a functionalized electrotextile for the detection of the avidin-biotin
binding reaction was previously done by Bhattacharyya et al. [13] where an electrospun nylon
fiber mat was coated with the conducting copolymer 3,4-ethylenedioxythiophene (EDOT) and 3thiopheneethanol (3-TE) using oxidative chemical vapor deposition in order to make the coating
conformal. This combination of reagents built a hydroxyl group into the polymer coating that
was then used for avidin attachment. The avidin bound fibers were then assessed both optically
and electrochemically for the attachment of biotin. Using the same methodologies as
Bhattacharyya et al. for optical and electrochemical detection (confocal microscopy of FITCavidin and biotinylated quantum dots and a simple electrochemical cell setup) the fiber
membranes developed in Chapter 3 were found to be able to successfully detect biotin in
solutions of 0.5, 5, 50, and 500 µM when a carboxyl functional group was present in a
polypyrrole based electrotextile as a result of the 3TAA inclusion in the polymer.

Five factors were identified as significant in the synthesis of the conductive polypyrrole – 3TAA
co-polymer. They were: concentration of pyrrole (monomer), concentration of 3TAA
(monomer), concentration of FeCl3 (oxidant), concentration of 5SSA (dopant), and
polymerization time. These process conditions must be carefully controlled in order to generate
a reproducible and effective polymer [121]. A central composite experimental design varying
these factors was conducted in order to develop a multiple regression model of the
148

polymerization reaction. The use of a central composite design is a well-established method for
performing regression / optimization analyses in many fields [165]. This includes the fields of
biosensor development and polymer synthesis. Multiple regression modeling has been used to
optimize biosensor performance based on influencing factors, such as signal output, structural
parameters, electrical parameters and bacterial concentrations [186-188]. The use of multiple
regression modeling is particularly useful for predicting and optimizing polymer properties
which are often complex and non-linear and therefore not easy to accurately predict [189]. The
effects of modifications to the polymerization process are difficult to predict because of the
possibility of a large number of interactions between reagents resulting in multiple changes to the
polymer. The use of multiple regression modeling and optimization has not been previously
used with electrotextile fibers for biosensor development. This model was used to find the
optimal conditions for each factor in order to generate the lowest experimentally possible
electrotextile resistance. The optimal conditions were: 58.79% volume of pyrrole, 0.03 g/mL of
3TAA, 0.11 M FeCl3, 0.25M 5SSA, and 70 minutes of reaction time to generate an electrotextile
with a predicted resistance of 35.41

. These conditions were experimentally verified and the

mathematical model was statistically validated. The use of a multiple regression model provided
a comprehensive means for not only optimizing but also characterizing the polymerization
reaction.

In order for the fibers to be used as a biosensor, the target pathogen must be captured / attached
to the fibers. E. coli O157:H7 specific antibodies were selected as the biorecognition element,
but the necessary concentration and the method of attachment still needed to be determined.
Antibody-antigen binding assays were then performed using standard attachment chemistry
149

techniques to determine the capture capability of the antibody-functionalized conductive
membranes [157]. Attachment and capture efficiency were determined through the use of
fluorescent labels and assessed using a fluorometer and confocal microscopy. The effect of
blocking the fibers was also assessed. Passive adsorption, covalent binding using
glutaraldehyde, and cross-linking using EDC and sulfo-NHS were all found to be effective
immobilization methods, however glutaraldehyde demonstrated the best antibody attachment.
While each of the tested methodologies is well established, analysis had not previously been
done on comparing their effectiveness for attachment to electrotextile surfaces. With their threedimensional, fibrous, porous structure, electrotextiles are a more complex attachment surface
than planar metal electrodes, metallic or plastic beads, or polystyrene plates. One of the biggest
issues that had to be assessed when performing attachment and capture on electrotextile surfaces
was non-specific binding which is a common issue with membranes and textiles [190, 191].
Blocking of the fibers using BSA or goat serum combined with a wash step was found to reduce
non-specific binding on the fibers. The tested scenario found to perform the best was using
glutaraldehyde to attach the antibodies at a concentration of 10 µg/mL with goat serum used to
block against nonspecific binding.

Granato et al. have previously shown that electrodes could be made from disposable electrospun
fibers that were coated with a conductive polypyrrole polymer [12]. It was demonstrated that
these electrodes could function as part of an electrochemical cell as a simple sensor system for
the detection of phosphate and carbonate organic anions. Bhattacharrya et al. has also
demonstrated that polymer coated fibers can function as an electrode in a simple electrochemical
cell to perform sensing (avidin-biotin reaction) [13]. Neither Granato or Bhattacharrya,

150

however, have adapted their sensors to evaluate their performance as functioning biosensors for
the detection of pathogens. In Chapter 6, the developed antibody-functionalized conductively
coated nonwoven fibers were used as the electrode transducer in the design of an electrochemical
biosensor for the detection of E. coli O157:H7. The proof of concept for the system was
established by measuring the resistance response of the fibers when they were exposed to a target
pathogen in solution over 15 minutes. By evaluating the system as a circuit with two resistors in
parallel, it was found that the resistance of the system not associated with the electrotextile fibers
was calculated to contribute only 2.8% of the total system resistance and was determined not to
be a significant contribution. The biosensor was shown to be able to differentiate between small
changes in conductivity due to the presence of the target pathogen in a solution over a
0

9

concentration range of 10 – 10 CFU/ mL. The sensor results were normalized for comparison
between experiments. The trends in the biosensor results were reproducible, however due to the
variations between lots and the difficulty in differentiating between positive samples, the
biosensor is not yet capable of being used as a quantitative detection system. The ability of the
biosensor to detect significant differences between samples that do and do not contain bacteria
indicates that the biosensor can work as a yes / no screening system. The results show that the
0

biosensor has an experimental lower limit of detection of 3.23 x 10 CFU/mL for the detection
of E. coli O157:H7 in pure culture.

In summary, this research demonstrates the successful integration of conductive polymer
synthesis, mathematical modeling and optimization of the polymerization process,
biorecognition element immobilization, and electrochemistry for the development of an
electrotextile based biosensor. The successful use of an electrotextile in the fabrication of an
151

electrochemical biosensor for the detection of a bacterial pathogen has the potential to result in
the creation of a light-weight, flexible, inexpensive, and disposable method for the detection of
various target pathogens. In Chapter 8 I will briefly discuss the necessary steps to advance this
work for field readiness.

152

Chapter 8 : Future work

Future work is required to fully optimize the biosensor detection system for real world use. In
order to improve the sensor for field readiness the variability of the sensor must be reduced
through the use of coating optimization, further analysis of antibody immobilization and
blocking techniques, and the development of an extraction method. After modifications to the
sensor and experimental procedures have been made, the sensor system will still need to be
tested to establish its functioning parameters such as limits of detection and specificities as well
as validated before any sort of field testing can be introduced.

The optimization of the polymerization process on the nonwoven fibers, described in Chapter 4,
and the blocking of the fibers using goat serum, described in Chapter 5, were the last
experiments to be completed and therefore the optimized fibers with goat serum block have not
been assessed in the biosensor yet. Optimizing the polymerization parameters in Chapter 4
appeared to reduce the variability in the initial electrotextile conductivity as well as lowering the
overall electrotextile resistance when compared to the original established coating from Chapter
3. Comparison experiments will need to be conducted to determine if this is true and determine
if there are any other coating parameters in the synthesis procedure that need to be optimized in
order to improve the base material. Because the initial uncoated fibers are variable in thickness
and direction, it is essential to try to reduce the variability between the coated fibers at every
other component and fabrication step in order to have a functional sensing material. Because the
fiber coating is heavier, has a different chemical makeup, and is more conductive than the
coating that was used for establishing the antibody attachment protocol (Chapter 5) and for
153

establishing the initial biosensor sensitivity (Chapter 6), these tests will need to be repeated with
the optimized fibers to establish if they need to be altered for the new fibers. The increase in
concentration of 3TAA in the optimized fibers may improve the capture efficiency when using
EDC / sulfo-NHS binding, resulting in the establishment of a new antibody attachment method
for the sensor. Because the coating is heavier on the fibers, it may be necessary to add more
wash steps in order to remove any excess unbound coating or nonspecifically attached
pathogens. In addition, the fibers will need to be tested with several different potential
electrolyte solutions in order to find one that allows the system to be conductive, but not reactive
with the coating. Finally, the effect of goat serum as a blocking agent on the fiber conductivity
will also need to be analyzed. While it was shown in Chapter 5 that a blocking agent is
necessary, the concentration could potentially be reduced if it is found to have a significant effect
on the electrotextile conductivity.

Although the current system has a low limit of detection, successfully detecting E. coli O157:H7
0

at levels as low as 10 CFU/ mL, this has only been done using pure culture that is added directly
to the system. An extraction protocol must be developed for the biosensor in order for it to be
useful in real world situations. One possibility is for the antibody-functionalized electrotextile
membrane to be placed into diluted samples and allowed to react for 5 minutes. The bacteria
within the sample will be captured by the antibodies attached to the surface and concentrated on
the membrane forming a bacterial layer. When the electrotextile membrane is removed from the
sample it will be rinsed in order to remove any non-specifically bound bacteria and inserted into
the electrochemical cell as the working electrode, where the change in conductivity will indicate
the presence or absence of the pathogen of interest. Another option would be to use
154

immunomagnetic separation to extract the target pathogen from the sample and then apply the
extract directly to the electrotextile in the biosensor.

The use of an antibody coated bead for performing a separation step before the sample is added
to the biosensor could also help with improving the biosensor specificity. Because only one type
of antibody is being used for detection the biosensor is subject to the specificity of that antibody
and has the potential for cross-reactivity with other non-target pathogens. Using a secondary
antibody on non-conductive nanoparticle beads that differed from the antibody used on the fibers
would help reduce the potential for false positives due to cross-reactivity. The non-conductive
nanoparticles would only be attached to the pathogen that bound to both the primary and
secondary antibodies and would create a second impedance layer. This second impedance layer
could also be used to improve biosensor sensitivity by amplifying the measured resistance when
the target pathogen is bound to the electrotextile surface.

The sensitivity of the biosensor could also be improved through the incorporation of an
incubation step. At low bacterial concentrations, the change in the resistance of the circuit may
be too low to differentiate from system noise. In order to increase system sensitivity at low
concentrations, a short (<6 hours) incubation period could be built into the system protocol. An
incubation of less than 6 hours allows for testing to still be conducted and a result generated
within 1 work day shift. The use of an incubation period is already used in the gold standard
methods of real-time PCR or plate counting recommended by the FDA [6].

155

Following the validation of the biosensor for detection with pure samples, a sensitivity study will
need to be conducted for detection of E. coli O157:H7 in inoculated food samples, such as
0

vegetables and fruits. The food samples may be inoculated with cell concentrations of 10 – 10

6

CFU/mL. Testing will then be conducted with an identical procedure to the one used to
determine the sensitivity of the biosensor with pure culture samples, as presented in Chapter 6.
To determine the specificity of the biosensor, pure culture samples of E. coli O157:H7 may be
tested and compared to the results of samples containing other common food and waterborne
pathogens such as enterotoxigenic Escherichia coli (ETEC) strains, Salmonella typhimurium,
Shigella sonnei, and Campylobacter jejuni. Samples would be prepared, diluted, plated and
tested in an identical method to the one used to determine pure culture sensitivity.

In order to establish the robustness of the studies that determined the sensitivities, specificities,
and limits of detection established for the sensor using varying food samples it will be necessary
to validate the sensor data by completing blind studies as well as across lab testing. This will
remove any potential experimenter’s bias from the results. In addition, the sensor will need to be
tested against other established comparative methods such as ELISA or PCR in order to
determine if the sensor will actually be beneficial for real world use.

The goal of this work is to eventually develop a handheld reader for the electrotextile electrodes
so that the biosensor can be used as a snap and read system where the electrotextile is exposed to
the sample, removed, and inserted into the reader to be read with minimal handling and steps inbetween. Once the sensor has been validated for food samples in the laboratory setting, the
handheld reader will have to be developed and the system will have to be revalidated with the
156

new reader under actual in field conditions. In order to complete a comprehensive validation, the
entire system will need to again be tested for sensitivity and specificity limits by using blind
studies, across lab testing, and comparative studies against established detection methodologies
under in field conditions. This will establish the feasibility and parameters for the successful use
of the biosensor for detection of E. coli O157:H7 in foods for military use.

In summary, this research demonstrates the successful integration of conductive polymer
synthesis, mathematical modeling and optimization of the polymerization process,
biorecognition element immobilization, and electrochemistry for the development of an
electrotextile based biosensor. More work is needed in order to create a biosensor that is viable
for being used in the field. This work includes reduction in material variability via optimization
and improvement of the electrotextile synthesis procedures, development of an extraction
protocol for measuring samples, establishing sensitivity and specificity limits for the sensor with
pure culture and food samples both in the lab and eventually in the field, and validating those
procedures and limits by using blind studies and multi-lab testing to prevent experimenter’s bias
in the results.

157

APPENDICES

158

APPENDIX A: STANDARD OPERATING PROCEDURES
A.1 FIBER SYNTHESIS PROTOCOL
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Polymerization of Doped Polypyrrole on Fibers
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Ferric Chloride (FeCl3)

b.

Pyrrole (C4H5N)

c.

3-Thiopheneacetic Acid, 3TAA (C6H6O2S)

d.

5-Sulfosalicylic Acid Hydrate, 5SSA (HO3SC6H3-2-(OH)CO2H•xH2O)

e.

Reagent Grade Water

II.

Equipment
a.

Glass staining box

b.

Scissors

c.

Pipettors

d.

10mL and 1mL Pipette Tips

e.

Forceps

f.

Oscillator or Rocker

g.

Punch and hammer

159

Procedure:
1. Measure and cut fiber sheet to dimensions of 6 cm x 8 cm so it can lay flat in reaction
container.
2. Soak fiber sample in pyrrole-3TAA solution (10% volume pyrrole to water with 10
mg/mL 3TAA) for 1 minute.
3. Lay wet sheet flat on bottom of glass staining box.
4. Add 30mL of 0.1 M FeCl3 in water solution to box.
5. Add 3mL of 0.1M 5SSA in water solution to box.
6. Secure lid and place on oscillator or rocker on low setting so liquid is moving over and
under fiber sample, but not washing it against the side of the box. Let to react for 30 min.
7. Remove sample and rinse both sides with water. Lay flat to dry.
8. If in chemical hood, flip after 15 min.
9. If on benchtop, flip after 1 hr.
10. Cut sample using punch and mallet for resistance measuring and testing.

160

A.2 OPTIMIZED FIBER SYNTHESIS PROTOCOL
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Optimized Polymerization of Doped Polypyrrole on Fibers
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Ferric Chloride (FeCl3)

b.

Pyrrole (C4H5N)

c.

3-Thiopheneacetic Acid, 3TAA (C6H6O2S)

d.

5-Sulfosalicylic Acid Hydrate, 5SSA (HO3SC6H3-2-(OH)CO2H•xH2O)

e.

Reagent Grade Water

II.

Equipment
a.

Glass staining box

b.

Scissors

c.

10mL and 1mL Pipette Tips

d.

Pipettors

e.

Forceps

f.

Oscillator or Rocker

g.

Punch and hammer

161

Procedure:
1. Measure and cut fiber sheet to dimensions of 6 cm x 8 cm so it can lay flat in reaction
container.
2. Soak fiber sample in pyrrole-3TAA solution (58.79% volume pyrrole to water with 0.03
g/mL 3TAA) for 1 minute.
3. Lay wet sheet flat on bottom of glass staining box.
4. Add 30mL of 0.11 M FeCl3 in water solution to box.
5. Add 3mL of 0.25 M 5SSA in water solution to box.
6. Secure lid and place on oscillator or rocker on low setting so liquid is moving over and
under fiber sample, but not washing it against the side of the box. Let to react for 70 min.
7. Remove sample and rinse both sides with water. Lay flat to dry.
8. If in chemical hood, flip after 15 min.
9. If on benchtop, flip after 1 hr.
10. Cut sample using punch and mallet for resistance measuring and testing.

162

A.3 ANTIBODY PASSIVE ADSORPTION PROTOCOL
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Antibody Immobilization, Passive Adsorption (96 well size)
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Sterile Distilled Water

b.

KPL Bactrace ® Goat Anti-E.coli O157:H7 diluted to desired concentration in 1x
PBS

c.

Blocking Agent (5% w/v Bovine Serum Albumin or 5% KPL Normal Goat Serum)

d.

Phosphate Buffered Saline (1x PBS)

e.

Phosphate Buffered Saline with 0.1% Tween-20 (PBST)

II.

Equipment
a.

Conductive Fiber Electrotextile

b.

Scissors

c.

Punch and Hammer

d.

96 well plate

e.

Pipette Tips, 1mL and 100 µL

f.

Pipettors

g.

Forceps

h.

Oscillator or Rocker

i.

Refrigerator
163

Procedure:
1. Cut nonwoven fibers into 96 well size discs using punch and hammer.
2. Put 1 disc into each well of a 96 well plate.
3. Wash nonwoven fiber discs with sterile water and dry for 10 minutes.
4. Add 250 µL of antibody at desired concentration to each disc.
5. Incubate fiber discs with antibody for 1 hr at room temperature with gentle agitation.
6. Remove antibody solution and apply 50 µL of blocking agent solution.
7. Allow fibers and blocking agent to react at room temperature with gentle agitation for 1
hour.
8. Wash fibers twice with PBS.
9. Wash fibers once with PBST and refrigerate until use.

164

A.4 ANTIBODY GLUTARALDEHYDE PROTOCOL (96 WELL PLATE)
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Antibody Immobilization, Glutaraldehyde (96 well size)
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Sterile Distilled Water

b.

Glutaraldehyde

c.

KPL Bactrace ® Goat Anti-E.coli O157:H7 diluted to desired concentration in 1x
PBS

d.

Deactivating Buffer (0.2 M Tris, 10 mM Sodium Cynaoborohydride)

e.

Blocking Agent (5% w/v Bovine Serum Albumin or 5% KPL Normal Goat Serum)

f.

Phosphate Buffered Saline (1x PBS)

g.

Phosphate Buffered Saline with 0.1% Tween-20 (PBST)

II.

Equipment
a.

Conductive Fiber Electrotextile

b.

Scissors

c.

Punch and Hammer

d.

96 well plate

e.

Pipette Tips, 1mL and 100 µL

f.

Pipettors

g.

Forceps
165

h.

Oscillator or Rocker

i.

Incubator

j.

Refrigerator

Procedure:
1. Cut nonwoven fibers into 96 well size discs using punch and hammer.
2. Put 1 disc into each well of a 96 well plate.
3. Wash nonwoven fiber discs with sterile water and dry for 10 min.
4. Add 25 µL of 2.5 mM glutaraldehyde to each disc and incubate for 1 hr at 4˚C.
5. Wash fibers with distilled water and dry for 10 min.
6. Add 250 µL of antibody at desired concentration to each disc.
7. Incubate fiber discs with antibody for 15 min at 37˚C.
8. Wash fibers with distilled water and dry for 10 min.
9. Add 50 µL of deactivating buffer and incubate for 15 min at 37˚C.
10. Wash fibers with distilled fibers and dry for 10 min.
11. Apply 50 µL of blocking agent solution.
12. Allow fibers and blocking agent to react at room temperature with gentle agitation for 1
hr.
13. Wash fibers twice with PBS.
14. Wash fibers once with PBST and refrigerate until use.

166

A.5 ANTIBODY GLUTARALDEHYDE PROTOCOL (2 CM X 2 CM SQUARE)
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Antibody Immobilization, Glutaraldehyde (2 cm x 2 cm squares)
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Glutaraldehyde

b.

KPL Bactrace ® Goat Anti-E.coli O157:H7 (10 µg/mL in 1x PBS)

c.

Deactivating Buffer (0.2 M Tris, 10 mM Sodium Cynaoborohydride)

d.

Blocking Agent (5% w/v Bovine Serum Albumin or 5% KPL Normal Goat Serum)

e.

Phosphate Buffered Saline (1x PBS)

f.

Phosphate Buffered Saline with 0.1% Tween-20 (PBST)

II.

Equipment
a.

Conductive Fiber Electrotextile

b.

Scissors

c.

6 well plate

d.

Pipette Tips, 1mL and 100 µL

e.

Pipettors

f.

Forceps

g.

Oscillator or Rocker

h.

Incubator

i.

Refrigerator
167

Procedure:
1. Cut nonwoven fibers into 2 cm x 2cm squares.
2. Put 1 square into each well of a 6 well plate.
3. Wash nonwoven fiber squares with 0.01 M PBS and dry for 10 min.
4. Add 3 mL of 2.5 mM glutaraldehyde to each well and incubate for 1 hr at 4˚C.
5. Wash fibers with 0.01 M PBS and dry for 10 min.
6. Add 4 mL of antibody to each square.
7. Incubate fiber squares with antibody for 15 min at 37˚C.
8. Wash fibers with 0.01 M PBS and dry for 10 min.
9. Add 3 mL of deactivating buffer and incubate for 15 min at 37˚C.
10. Wash fibers with 0.01 M PBS and dry for 10 min.
11. Apply 3 mL of blocking agent solution.
12. Allow fibers and blocking agent to react at room temperature with gentle agitation for 1
hr.
13. Wash fibers twice with PBS.
14. Wash fibers once with PBST and refrigerate until use.

168

A.6 ANTIBODY EDC / SULFO-NHS PROTOCOL (96 WELL PLATE)
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Antibody Immobilization, EDC / Sulfo-NHS (96 well size)
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Chemicals
a.

Sterile Distilled Water

b.

N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide Hydrochloride (EDC)

c.

N-hydroxysulfosuccinimide (Sulfo-NHS)

d.

2-(N-morpholino)ethanesulfonic Acid Buffer (MES), 50 mM

e.

KPL Bactrace ® Goat Anti-E.coli O157:H7 diluted to desired concentration in 1x
PBS

f.

Blocking Agent (5% w/v Bovine Serum Albumin or 5% KPL Normal Goat Serum)

g.

Phosphate Buffered Saline (1x PBS)

h.

Phosphate Buffered Saline with 0.1% Tween-20 (PBST)

II.

Equipment
a.

Conductive Fiber Electrotextile

b.

Scissors

c.

Punch and Hammer

d.

96 well plate

e.

Pipette Tips, 1mL and 100 µL

f.

Pipettors
169

g.

Forceps

h.

Oscillator or Rocker

i.

Refrigerator

Procedure:
1. Cut nonwoven fibers into 96 well size discs using punch and hammer.
2. Put 1 disc into each well of a 96 well plate.
3. Wash nonwoven fiber discs with sterile water and dry for 10 min.
4. Add 200 µL of EDC and Sulfo-NHS in MES buffer at manufacturer’s recommended
concentrations to fibers and react with gentle agitation at room temperature for 15
minutes.
5. Wash fibers twice with MES buffer.
6. Add 250 µL of antibody at desired concentration to each disc.
7. Incubate fiber discs with antibody with gentle agitation for 4 hrs at room temperature.
8. Wash fibers with MES buffer.
9. Apply 50 µL of blocking agent solution.
10. Allow fibers and blocking agent to react at room temperature with gentle agitation for 1
hr.
11. Wash fibers twice with PBS.
12. Wash fibers once with PBST and refrigerate until use.

170

A.7 PICOGREEN STAINING OF CELLS
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: PicoGreen Staining of Bacterial Cells
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Reagents
a.

Full Grown Bacterial Culture in Growth Medium Broth

b.

PicoGreen dsDNA stain

c.

Sterile Distilled Water

II.

Equipment
a.

2 mL Centrifuge Tubes

b.

Pipette Tips, 10 mL, 1 mL and 10 µL

c.

Pipettors

d.

Centrifuge

e.

Vortex

f.

Oscillator

Procedure:
1. Remove 1 mL of cell culture and pellet in centrifuge for 3 min at 5,000 x g.
2. Remove supernatant and resuspend pellet in 5 µL PicoGreen and 500 µL water, vortex.
3. Place sample in rotating oscillator and rotate for 5 min at room temperature with no light
while stain cross-links with cells.
171

4. Pellet stained cells in centrifuge for 3 min at 5,000 x g.
5. Remove supernatant and resuspend pellet with 1 mL of water, vortex.
6. Dilute stained cells to necessary volume / concentration with water.

172

A.8 FLUORESCENT MEASUREMENT OF CAPTURED STAINED CELLS
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Measuring Fluorescence of Captured Cells (96 well plate)
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Reagents
7

a.

PicoGreen Stained Bacteria Cells (approximate concentration of 10 CFU/mL)

b.

Phosphate Buffered Saline (1x PBS)

II.

Equipment
a.

Conductive electrotextile fibers (96 well plate size) with immobilized antibodies

b.

96 well plate (black)

c.

Pipette Tips, 1 mL

d.

Pipettors

e.

Victor3 MuliLabel Counter

Procedure:
1. Place antibody functionalized fibers into wells of 96 well plate.
2. Add 200 µL of PicoGreen stained cells to each well.
3. Incubate fibers with cells for 15 min at 37˚C.
4. Wash fibers 3 times with PBS.
5. Pipette all excess liquid off of each fiber, make sure fibers are lying flat in the bottom of
each well.
173

6. Place plate with fibers into Victor.
7. Read fluorescence output of plate using FITC program, excitation of 490 nm and
emission of 535 nm.

174

A.9 ELECTROCHEMICAL MEASUREMENT USING BIOSENSOR
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Measuring Resistance Across Fibers in Biosensor
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Reagents
a.

Sterile Distilled Water

b.

Bacterial Serial Dilutions (Cells Washed Using Centrifuge and Resuspended in
Butterfield’s Phosphate Buffer)

c.

Butterfield’s Phosphate Buffer (Blank)

d.

Phosphate Buffer (0.1 M PB)

e.

Bleach

f.

Ethanol

II.

Equipment
a.

Conductive electrotextile fibers (2 cm x 2 cm) with immobilized antibodies

b.

Glass beaker

c.

Pipettors

d.

Pipette Tips, 10 mL and 1 mL

e.

Alligator Clip Leads

f.

Potentiostat

175

Procedure:
1. Turn on potentiostat and attach alligator clip leads.
2. Place leads into beaker with 20 mL of distilled water and secure leads to opposing sides
of beaker.
3. Run potentiostat for constant potential amperometry with a constant voltage of 0.5 V for
30 min – 2 hr to warm up system, taking a measurement every minute.
4. If result at end of warm up is a horizontal line, system is ready for use.
5. Empty beaker.
6. Attach electrotextile fibers to alligator clip leads, with each lead attached at opposing
rd

sides, facing each other, going into the fibers to the depth of the 3 tooth.
7. Insert fibers with alligator clip leads into glass beaker.
8. Attach alligator clip leads to the potentiostat.
9. Secure wires to opposing sides of beaker so fibers are lying flat on bottom of the beaker
and pulled taut, but not being strained.
10. Submerge fibers in 18 mL of PB.
11. Add 2 mL of sample and stir gently with pipette tip to mix sample with solution.
12. Run potentiostat using constant potential amperometry at a constant voltage of 0.5 V for
15 min taking a measurement every 30 s.
13. Record measured current over time.
14. Clean glassware, alligator clip leads, and forceps between different bacterial
concentration samples.
15. Repeat for each sample.

176

A.10 BACTERIAL CULTURE TRANSFER AND GROWTH
Project: Conductive Polymer Coated Non-Woven Fiber Based Biosensor
Protocol: Transfer of E. coli O157:H7 Sakai Strain Cultures and Growth
PPE: BSL-1; standard lab coat and latex or nitrile gloves, long pants, close-toed shoes, protective
eye wear
Materials:
I.

Reagents
a.

Trypticase Soy Broth (TSB)

b.

CHROMagar O157 plate with E. coli O157:H7 Sakai Strain Culture

II.

Equipment
a.

Biosafety cabinet

b.

Vortex

c.

15 ml Sterile Tubes

d.

Inoculating Loop

e.

1000 µl pipette

f.

1000 µl pipette tips with filter (sterile)

Procedure (all in biosafety cabinet):
1. Remove CHROMAGAR O157 plate with bacterial culture from refrigerator and remove
one colony from plate using inoculating loop.
2. Transfer colony to 9 mL of sterile TSB.
3. Incubate at 37˚C for 48 hrs to bring culture to maximum concentration.
4. Vortex 48 hr microbial culture.

177

5. Transfer 1 mL of the culture into 9 mL of sterile TSB.
6. Vortex
7. Incubate the new culture tube for 4 hrs at 37°C.

178

APPENDIX B: DATA
B.1 CHAPTER 3 DATA

Figure B- 1. SEM image of Nylon 6fibers with 3
3-COOH and pyrrole polymer coating,
acetonitrile solvent, 18 hour reaction time. Scale bar equal to 100 µm, magnification at 500x,
EHT of 30 kV and working distance of 6.0 mm.

179

Figure B- 2. SEM image of Nylon -6 fibers with 3-COOH and pyrrole polymer coating,
.
COOH
acetonitrile solvent, 18 hour reaction time. Scale bar equal to 2 µm, magnification of 30,000x,
EHT of 30 kV and working distance of 6.0 mm.

180

Figure B- 3. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, acetonitrile
Nylon-6
solvent, 18 hour reaction time. Scale bar equal to 100 µm, magnification of 150x, EHT of 30 kV
and working distance of 6.0 mm.

181

Figure B- 4. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, acetonitrile
Nylon-6
solvent, 18 hour reaction time. Scale bar equal to 2 µm, magnification of 20,000x, EHT of 30
.
kV and working distance of 6.0 mm.

182

Figure B- 5. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, methanol
.
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. Scale bar equal to 100 µm, magnification of
200x, EHT of 30 kV and working distance of 6.0 mm.

183

Figure B- 6. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, water
.
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. Scale bar equal to 20 µm, magnification of
500x, EHT of 30 kV and working distance of 8.0 mm.

184

Figure B- 7. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, water
.
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. No rinse. Scale bar equal to 2 µm,
magnification of 10,000x, EHT of 30 kV and working distanc of 6.0 mm.
distance

185

Figure B- 8. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, water
.
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 10 µm,
magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

186

Figure B- 9. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, water
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse and sonication. Scale bar equal
Scal
to 2 µm, magnification of 10,000x, EHT of 30 kV and working distance of 6.0mm.

187

Figure B- 10. SEM image of Nylon 6 fibers with 3TAA and pyrrole polymer coating, water
.
Nylon-6
solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 100 µm,
magnification of 500x, EHT of 30 kV and working distance of 6.0 mm.

188

Figure B- 11. SEM image of polypropylene fibers with 3TAA and pyrrole polymer coating,
.
water solvent, 5SSA dopant, 30 minute reaction time. DI water rinse. Scale bar equal to 100
µm, magnification of 500x, EHT of 30 kV and working distance of 6.0 mm.

189

Figure B- 12. SEM image of fibers with polymer coating containing 0 mg/mL 3TAA. Scale bar
.
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

190

Figure B- 13. SEM image of fibers with polymer coating containing 0 mg/mL 3TAA. Scale bar
.
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm.

191

Figure B- 14. SEM image of fibers with polymer coating containing 0.1 mg/mL 3TAA. Scale bar
.
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

192

Figure B- 15. SEM image of fibers with polymer coating containing 0.1 mg/mL 3TAA. Scale
.
bar equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm.
distance

193

Figure B- 16. SEM image of fibers with polymer coating containing 1 mg/mL 3TAA. Scale bar
equal to 10 µm, magnification of 5,000x, EHT of 30 kV and working distance of 6.0 mm.

194

Figure B- 17. SEM image of fibers with polymer coating containing 1 mg/mL 3TAA. Scale bar
.
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm.

195

Figure B- 18. SEM image of fibers with polymer coating containing 10 mg/mL 3TAA. Scale bar
equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm.

196

Figure B- 19. SEM image of fibers with polymer coating containing 100 mg/mL 3TAA. Scale
.
containing
bar equal to 200 µm, magnification of 100x, EHT of 30 kV and working distance of 6.0 mm.

197

Table B- 1. Data for Figure 3-7.
3TAA
Concentration
(mg/mL)
0.0
0.1
1.0
10.0
20.0
50.0
100.0

3TAA
Concentration
(mg/mL)
0.0
0.1
1.0
10.0
20.0
50.0
100.0

3TAA
Concentration
(mg/mL)
0.0
0.1
1.0
10.0
20.0
50.0
100.0

Resistivity
Run 1
5.19
3.13
3.6
6.51
8.22
10.38
2519.13

Run 2 Run 3
5.95
5.44
3.65
4.32
3.45
3.7
6.55
6.6
7.73
7.17
10.67 12.44
76.9 769.32

Run 4
3.8
4.46
3.01
4.77
7.69
8.56
1530.53

Resistivity
Run 6
Run 7 Run 8
Run 9
6.19
3.37
4.04
3.41
5.79
3.84
3.71
3.61
3.01
3.78
3.25
3.75
5.11
7.04
7.37
7.38
7.04
8.5
7.86
12.79
8.14
7.21
8.88
7.62
1229.93 693.67 2784.62 4071.96

Average
Resistivity
4.552222
4.575556
3.43
6.293333
8.136667
9.443333
1587.449

Standard
Deviation
1.135053
1.713433
0.300915
0.986927
1.871042
1.774197
1289.793

198

Run 5
3.58
8.67
3.32
5.31
6.23
11.09
610.98

Table B- 2. Data for Figure 3-8.
3TAA
Concentration
(mg/mL)
0.0
0.1
1.0
10.0
20.0
50.0
100.0

Sulfur Weight
Percent at 100 x
magnification (%)
0.93
0.78
0.55
1.3
1.54
2.24
3.83

199

Table B- 3. Data for Figure 3-9.
3TAA
Concentration
(mg/mL)
0.0
0.1
1.0
10.0
20.0
50.0
100.0

Fluorescent Output (RFU)
Run 1
1.015
1.092
1.432
1.102
1.102
1.235
6.683

Run 2
1.002
1.09
1.135
1.66
1.233
1.632
2.845

Run 3
1.069
1.103
1.594
1.669
1.468
2.069
2.359

200

Average
Fluorescent
Output
1.0287
1.0950
1.3870
1.4770
1.2677
1.6453
3.9623

Standard
Deviation
0.035529
0.007
0.232785
0.324791
0.185446
0.41716
2.368664

Table B- 4. Student’s T-Test of Resistivities (2 tails, α = 0.05)
Sample 1
0.0
mg/mL

Sample 2
0.1
mg/mL

P value

Significant Difference

0.973

No

201

Table B- 5. Data for Figure 3-12, resistances.

Time
(min)
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6

Blank
71355.76
73035.53
73915.68
73895.19
73597.06
73556.45
73356.03
73202.61
74298.15
76065.25
77368.64
77747.06
77461.47
76126.06
75211.70
75331.14
75986.29
76878.72
78480.28
79675.60
80121.32
81496.03
82290.01
81383.52
84210.53
58079.23
57767.69
58403.91
59585.88
58460.00
57427.65
58061.17
58295.69
58369.81
57937.42
58162.48

0.5 µM
62752.13
62625.81
62596.41
62708.56
64128.99
65166.29
66550.99
66907.21
68382.75
68302.68
68936.65
69473.73
70873.51
70871.72
71190.66
71776.46
73261.98
74064.76
74300.11
73929.83
73675.00
73371.89
72000.25
70546.73
72727.27
55522.51
55801.90
55744.69
55991.04
56198.94
56063.91
55913.89
56150.48
56207.97
55932.88
55734.43

Resistance ( )
Biotin Concentration
5 µM
50 µM
118946.49 86580.07
113119.89 88133.45
120872.01 85937.01
114552.23 85005.61
112130.07 86433.08
112848.63 87412.59
115193.39 87672.60
116932.37 89694.71
117037.28 93057.26
116354.86 95037.67
115400.63 96068.07
115001.72 97137.89
115031.78 99065.95
116047.86 100250.63
116260.22 101567.04
116255.55 101298.79
115356.41 102448.14
114550.00 102808.88
113534.98 102189.41
113353.44 109339.11
112855.89 124431.16
111544.90 137392.27
112182.63 136066.97
116026.11 134273.25
118500.96 136565.38
54448.49 56145.98
54836.38 56044.83
54412.52 56186.26
54054.05 56457.30
53724.49 57007.61
53745.90 57450.03
53621.36 57582.07
53535.75 57464.18
53445.31 57601.31
54260.94 57627.69
54502.27 57625.09
202

500 µM
111731.84
122860.90
125910.62
124036.48
124593.95
121601.66
119535.49
119750.76
124257.22
126885.13
129810.52
131352.09
129413.32
128504.38
128820.63
127323.72
124668.16
125975.81
131283.59
136365.86
137767.99
137297.31
138735.78
140795.49
139664.80
52434.46
53359.00
53784.09
53685.93
53750.79
53850.36
53899.79
53985.59
54105.69
54188.00
54244.64

Table B-5 (cont’d).
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0

57770.07
58062.37
58259.29
58141.95
57019.51
55799.12
55105.90
55022.46
55112.68
54830.39
54690.70
55189.47
56022.41
56219.26
181971.77
160256.41
170161.04
172498.73
173826.64
174939.56
176436.24
174883.02
173334.02
170306.98
167230.51
163293.89
162691.36
160963.00
161019.50
160987.30
163394.85
165539.67
168561.98
168714.58
168672.63
167475.12
164890.14
160998.19
161812.30

55902.38
56186.10
56236.19
56182.53
56321.76
56603.96
56959.64
57154.99
57187.20
57081.69
57014.86
56953.98
56777.85
56377.72
506329.11
425693.50
435036.06
426374.14
403095.13
374231.61
359008.93
352430.61
346080.40
346500.35
345331.29
347442.03
346394.72
345110.58
344717.51
347542.01
348518.03
345995.86
340045.14
338453.92
338173.73
335607.86
332258.59
335429.77
346320.35

54417.80
54120.85
54355.21
54363.33
54388.73
54519.19
54777.10
55021.17
55243.99
55641.11
55953.00
56090.30
55983.20
55401.66
434108.42
386206.85
416976.97
376141.78
344742.71
321137.80
326682.94
328304.15
333086.85
332044.78
330594.25
329933.91
329830.10
330359.66
329696.78
335106.04
333244.02
333835.64
331914.73
341359.37
341018.29
343769.25
346470.33
351957.77
336275.75
203

57564.60
57549.02
57772.38
58150.56
58675.52
58763.67
59064.25
59790.73
60959.68
60659.88
59886.64
60216.35
61871.62
61208.87
534351.14
459581.63
479780.61
438527.63
389158.97
361944.04
372093.02
375798.57
376582.02
372464.17
370870.63
369895.81
370059.52
366242.95
364112.23
361521.14
360769.62
360176.05
360885.76
363418.05
365924.52
367776.32
372179.44
377180.58
371574.55

54298.01
54323.84
54648.06
54727.05
55007.66
55279.43
55599.68
55386.38
55271.25
55308.98
55306.18
55536.61
55904.96
55594.16
546875.01
479452.12
506924.95
486956.39
430272.60
387061.01
374456.66
382336.07
376000.35
371402.04
367097.49
372761.84
377506.07
384673.38
388069.50
384546.76
376015.62
367894.58
368139.99
375441.90
384911.47
387813.05
383877.16
377358.49
378787.88

Table B- 6. Data for Figure 3-12, responses.

Time
(min)
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6

0.5 µM
-12.06
-14.25
-15.31
-15.14
-12.86
-11.41
-9.28
-8.60
-7.96
-10.21
-10.90
-10.64
-8.50
-6.90
-5.35
-4.72
-3.59
-3.66
-5.33
-7.21
-8.05
-9.97
-12.50
-13.32
-13.64
-4.40
-3.40
-4.55
-6.03
-3.87
-2.37
-3.70
-3.68
-3.70
-3.46
-4.17

Response (%)
Biotin Concentration
5 µM 50 µM 500 µM
66.70
21.34
56.58
54.88
20.67
68.22
63.53
16.26
70.34
55.02
15.04
67.85
52.36
17.44
69.29
53.42
18.84
65.32
57.03
19.52
62.95
59.74
22.53
63.59
57.52
25.25
67.24
52.97
24.94
66.81
49.16
24.17
67.78
47.92
24.94
68.95
48.50
27.89
67.07
52.44
31.69
68.80
54.58
35.04
71.28
54.33
34.47
69.02
51.81
34.82
64.07
49.00
33.73
63.86
44.67
30.21
67.28
42.27
37.23
71.15
40.86
55.30
71.95
36.87
68.59
68.47
36.33
65.35
68.59
42.57
64.99
73.00
40.72
62.17
65.85
-6.25
-3.33
-9.72
-5.07
-2.98
-7.63
-6.83
-3.80
-7.91
-9.28
-5.25
-9.90
-8.10
-2.48
-8.06
-6.41
0.04
-6.23
-7.65
-0.83
-7.17
-8.17
-1.43
-7.39
-8.44
-1.32
-7.31
-6.35
-0.53
-6.47
-6.29
-0.92
-6.74
204

Table B-6 (cont’d).
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0

-3.23
-3.23
-3.47
-3.37
-1.22
1.44
3.36
3.88
3.76
4.11
4.25
3.20
1.35
0.28
178.25
165.63
155.66
147.18
131.89
113.92
103.48
101.52
99.66
103.46
106.50
112.77
112.92
114.40
114.08
115.88
113.30
109.01
101.73
100.61
100.49
100.39
101.50
108.34
114.03

-5.80
-6.79
-6.70
-6.50
-4.61
-2.29
-0.60
0.00
0.24
1.48
2.31
1.63
-0.07
-1.45
138.56
140.99
145.05
118.05
98.33
83.57
85.16
87.73
92.16
94.97
97.69
102.05
102.73
105.24
104.76
108.16
103.95
101.67
96.91
102.33
102.18
105.27
110.12
118.61
107.82

-0.36
-0.88
-0.84
0.01
2.90
5.31
7.18
8.67
10.61
10.63
9.50
9.11
10.44
8.88
193.65
186.78
181.96
154.22
123.88
106.90
110.89
114.89
117.26
118.70
121.77
126.52
127.46
127.53
126.13
124.56
120.80
117.58
114.10
115.40
116.94
119.60
125.71
134.28
129.63

-6.01
-6.44
-6.20
-5.87
-3.53
-0.93
0.90
0.66
0.29
0.87
1.13
0.63
-0.21
-1.11
200.53
199.18
197.91
182.30
147.53
121.25
112.23
118.62
116.92
118.08
119.52
128.28
132.04
138.98
141.01
138.87
130.13
122.24
118.40
122.53
128.20
131.56
132.81
134.39
134.09
205

Table B- 7. Data for Figure 3-12, average response.

Time
(min)
180.3
210.3
240.3
270.4
300.4
330.4
360.4
390.5
420.5
450.5
480.6
510.6
540.6
570.6
600.7
630.7
660.7
690.8
720.8
750.8
780.8
810.9
840.9
870.9
901.0

Average Response (%)
Biotin Concentration
0.5 µM 5 µM 50 µM 500 µM
53.93
66.33 70.55
82.46
49.33
63.60 68.16
86.59
45.26
67.25 64.81
86.78
42.00
54.60 54.67
80.08
38.39
47.53 46.28
69.59
33.38
43.53 41.92
60.11
30.17
44.85 43.20
56.01
29.75
46.43 45.33
58.27
29.33
47.08 47.06
58.95
29.93
47.20 47.70
59.47
30.48
46.85 48.34
60.19
32.97
48.05 50.37
63.74
33.73
48.15 51.49
64.22
34.68
50.33 52.80
67.20
35.12
50.94 53.73
68.80
36.65
52.62 53.98
68.12
37.05
51.16 53.64
64.42
36.24
50.02 52.83
62.33
33.43
47.19 50.99
62.11
32.39
48.28 54.41
64.66
32.18
48.17 60.96
67.01
31.56
48.15 65.90
67.05
30.73
49.36 66.72
67.34
32.13
53.70 69.90
69.06
33.56
49.03 66.89
66.28

206

Table B- 8. Student’s T-Test of Biotin Sensor Response (2 tails, α = 0.05)

Sample 1
Blank
Blank
Blank
Blank

Sample 2
0.5 µM
5 µM
50 µM
500 µM

P value
2.174E-06
2.328E-14
2.785E-12
1.382E-14

Significant
Difference
Yes
Yes
Yes
Yes

207

B.2 CHAPTER 4 DATA
Table B- 9. Data for Table 4-2 and Table 4-3.

Run*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Pyrrole
(%)
C1
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5

Experimental Factors
3TAA
FeCl3
5SSA
(g/mL)
(M)
(M)
C2
C3
C4
0.0125
0.075
0.15
0.0125
0.075
0.35
0.0125
0.125
0.15
0.0125
0.125
0.35
0.0375
0.075
0.15
0.0375
0.075
0.35
0.0375
0.125
0.15
0.0375
0.125
0.35
0.0125
0.075
0.15
0.0125
0.075
0.35
0.0125
0.125
0.15
0.0125
0.125
0.35
0.0375
0.075
0.15
0.0375
0.075
0.35
0.0375
0.125
0.15
0.0375
0.125
0.35
0.025
0.1
0.25
0.025
0.1
0.25
0
0.1
0.25
0.05
0.1
0.25
0.025
0.05
0.25
0.025
0.15
0.25
0.025
0.1
0.05
0.025
0.1
0.45
0.025
0.1
0.25
0.025
0.1
0.25
0.025
0.1
0.25
0.025
0.1
0.25
0.025
0.1
0.25
0.025
0.1
0.25
0.0125
0.075
0.15
0.0125
0.075
0.35
0.0125
0.125
0.15
208

Response
Time
(min)
C5
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5

R for
regression
y
0
1
1
1
0
1
0
0
0
1
0
0
0
1
1
1
0
1
1
0
0
0
0
0
0
1
1
0
0
1
0
0
1

Resistance
( )
53.8
36.5
23.3
40.2
82.6
49.8
53.2
96.8
267
32.2
65.6
50.8
55.8
29.5
34.1
27.1
84.8
42.6
30
92.4
81.8
62.2
92.8
56.1
84.6
47
44.5
85.2
52.7
37
75.2
107
25.9

Table B-9 (cont’d).
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72

32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5

0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125

0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125

0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
209

97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5

1
0
0
0
1
0
0
0
1
1
0
1
1
1
1
0
0
0
1
0
0
1
1
1
0
1
1
0
0
1
1
0
0
0
0
0
1
0
1

43.6
71.2
117.6
66.2
49.3
339
82.3
59.5
31.6
40.6
51.6
48
26.4
48.7
30.4
86.2
71.1
134.9
45.4
115.9
73.4
42
31.5
38.6
61.4
49.1
44.4
112.9
75.4
36.5
20.1
181
101.7
86.1
59.2
331
49.6
54.9
39.9

Table B-9 (cont’d).
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111

77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55

0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025

0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05

0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
210

97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75

0
1
1
1
0
0
1
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
0
1
1
0
1
1
1
0
0
0
0

68.6
39.9
49.6
38.6
84.1
52.2
36.1
87.8
82.5
28.8
102.8
72.2
68.4
51.8
42.5
52.6
54.5
62.5
46.6
71.3
67.8
58.9
32.4
72.5
99.8
64.4
226
59.5
62.2
46.3
41.4
51.5
45.7
31.1
47.8
99.7
63.4
90.6
58.4

Table B-9 (cont’d).
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150

55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55

0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025

0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1

0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
211

75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75

1
1
1
1
1
0
1
1
1
0
0
1
1
0
0
0
0
0
0
1
0
1
0
1
1
0
1
0
0
0
0
0
0
0
0
0
1
1
0

27.6
47.2
29.5
26.8
40.9
80.6
39
37.1
42.9
66.5
62.7
26.2
46.7
93.1
116.9
72.1
52.9
235
78.4
28.8
70.4
44.3
52.1
49.8
29.3
60.6
37.4
62.6
51.2
102.6
55.5
93.8
59.5
58.6
51.2
76.1
28.7
31.5
54.7

Table B-9 (cont’d).
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189

32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5

0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125

0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075

0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
212

97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5

1
0
1
1
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
1
1
1
0
1
0
1
1
1
0
1
0
1
0
0
0
0
0

39.9
65.3
27.2
20.9
120.6
109.5
65.2
80.3
136
102.6
83.6
24.1
53.5
67.8
38.5
26.2
77.8
79.8
57.1
61.9
102.9
45.9
47.9
48.9
67.7
39.7
54.2
36.6
39.6
36.4
123.2
48.2
68.3
38.6
73.4
118.2
53.5
71.5
181.8

Table B-9 (cont’d).
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228

77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100

0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025

0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1

0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
213

97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75

0
1
0
1
1
1
1
0
1
0
1
0
1
1
1
0
0
1
1
1
1
0
0
0
1
0
0
0
0
0
0
0
1
1
0
0
1
0
0

58.6
28.9
60.2
31.1
37.8
29.4
33.9
87.7
32.2
78.1
48.9
94.7
28.9
41.2
32.1
92.5
63.2
38.1
46.6
35.2
36.4
52.9
86.6
51.6
42.9
123.5
132.7
86.9
54.6
177.8
107.2
71.1
40.4
47.6
79.6
57.4
48.4
80.6
52.9

Table B-9 (cont’d).
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267

55
55
55
55
55
55
55
55
55
55
55
55
32.5
32.5
32.5
32.5
32.5
32.5
32.5
32.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
77.5
10
100
55
55
55
55
55
55
55
55
55

0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.025
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.0125
0.0125
0.0125
0.0125
0.0375
0.0375
0.0375
0.0375
0.025
0.025
0
0.05
0.025
0.025
0.025
0.025
0.025
0.025
0.025

0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.075
0.075
0.125
0.125
0.1
0.1
0.1
0.1
0.05
0.15
0.1
0.1
0.1
0.1
0.1

0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
0.25
0.25
0.25
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.15
0.35
0.25
0.25
0.25
0.25
0.25
0.25
0.05
0.45
0.25
0.25
0.25
214

75
75
75
75
75
75
30
120
75
75
75
75
97.5
52.5
52.5
97.5
52.5
97.5
97.5
52.5
52.5
97.5
97.5
52.5
97.5
52.5
52.5
97.5
75
75
75
75
75
75
75
75
30
120
75

1
1
0
1
0
1
1
0
1
1
1
1
0
0
0
1
0
0
0
0
0
0
1
1
1
0
1
1
0
1
1
0
0
1
0
0
0
1
1

42.4
40.2
105.4
31.8
78.6
40.5
48.2
50.6
46.8
25.5
40.8
32
93.2
95.3
82.8
38.1
141.7
175.7
60.4
72.9
148.8
122.6
42.2
33.8
25.5
63.7
27.9
43.9
117.6
38.8
41.3
76.2
94.6
36.1
57.9
92.6
72.6
45.7
42.5

Table B-9 (cont’d).
268
269
270

55
55
55

0.025
0.025
0.025

0.1
0.1
0.1

0.25
0.25
0.25

215

75
75
75

1
1
1

34.9
36.5
49.8

Table B- 10. T-Test for model verification.
Hypothesis: 35.41 = 38.00
df
9
n
10
ȳ
38
µ
35.41
s
6.01
δ= s/sqrt(n)
1.90
t calc =(ȳ-µ)/δ
1.36
t tab (α = 0.025, df =9) 2.26
t calc < t tab
do not reject hypothesis

216

B.3 CHAPTER 5 DATA

Kd

1

2

3

4

5

6

7

8 9

10

260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 20. Western blot 1, where 1: ladder; 2: negative for E.coli
O157:H19GT164; 3: negative for E.coli O157:H7GT126; 4: negative for E.coli
O157:H7GT125; 5: positive for E.coli O157:H7 Sakai strain; 6: negative for E.coli
O157:H7 AEEC strain; 7: negative for E.coli O26:H11; 8: negative for E.coli
O55:H7; 9: positive for E.coli O157:H7 Spinach pGFPuv; and 10: negative for
Shigella boydii.

217

1

2

3

4

5

6

7

8

9

10

Kd
260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 21. Western blot 2, where 1: ladder; 2: negative for E.coli
O157:H38 Roe 1A164; 3: positive for E.coli O157:H45 166; 4: negative for
E.coli Mastitis 1368; 5: negative for Bacillus cereus; 6: negative for
Bacillus anthracis Sterne Strain; 7: positive for Citrobacter freundii
GT4885; 8: negative for Bacillus thuringiensis; 9: negative for Bedmark
generic E.coli K12; 10: negative for Enterobacter agglomerans GT1611.

218

Kd
260

1

2

3

4

5

6

7

8

9

10

160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 22. Western blot 3, where 1: ladder; 2: positive for SNP 17 E.coli
O157:H7; 3: positive for SNP 18 E.coli O157:H7, 4: positive for SNP 19
E.coli O157:H7, 5: positive for SNP 20 E.coli O157:H7, 6: positive for
EHEC1 #1 E.coli O157:H7, 7: positive for EHEC1 #2 E.coli O157:H7; 8:
positive for EHEC1 #3 E.coli O157:H7; 9: positive for EHEC1 #4 E.coli
O157:H7, and 10: positive for EHEC1 #5 E.coli O157:H7.

219

1

2

3

4

5

6

7

8

9

10

Kd
260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 23. Western blot 4, where 1: ladder; 2: negative for Pseudomonas
aeruginosa; 3: positive for Escherichia hermannii; 4: negative for Staphylococcus
aureus 12600; 5: negative for Staphylococcus aureus Ent AT #4; 6: positive for
Enterococcus fecalis ATCC 19433; 7: negative for Staphylococcus aureus ATCC
25923; 8: positive for Citrobacter freundii ATCC 8090; 9: positive for EHEC1 #8
E.coli O157:H7; 10: positive for E.coli O157:H7 Spinach TW 14359.

220

1

2

3

4

5

6

7

8

9 10

Kd
260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 24. Western blot 5, where 1: ladder; 2: positive for EHEC1 #6 E.coli O157:NM;
3: positive for EHEC1 #7 E.coli O157:NM; 4: positive for E.coli O157:H7 Ao317; 5:
negative for Shigella flexneri; 6: negative for E.coli O26:H11 BSL 326; 7: positive for
Citrobacter freundii ATCC 8090; 8: negative for Salmonella enteritis Typhimurium 0648
10/12; 9: negative for Klebsiella pneumoniae 6-21; 10: negative for Klebsiella
pneumoniae ATCC 13883.

221

Kd

1

2

3

4

5

6

7 8

9

10

260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 25. Western blot 6, where 1: ladder; 2: positive for E.coli O157:H43 GT
4316; 3: negative for E.coli O157:H19 GT164; 4: negative for Enterobacter
agglomerans GT1611; 5: positive for E.coli O157:H7 GT632; 6: negative for
E.coli O157:H7 GT126; 7: negative for E.coli O157:H7 GT125; 8: positive for
E.coli O157:NM GT4141; 9: positive for E.coli O157:H7 A110; 10: positive for
Citrobacter freundii (CF3) GT5142.

222

1

2

3

4

5

6

7

8

9

10

Kd
260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 26. Western blot 7, where 1: ladder; 2: positive for SNP2 E.coli
O157:H7; 3: positive for SNP3 E.coli O157:H7; 4: positive for SNP4 E.coli
O157:H7; 5: positive for SNP5 E.coli O157:H7; 6: positive for SNP6 E.coli
O157:H7; 7: positive for SNP7 E.coli O157:H7; 8: positive for SNP8 E.coli
O157:H7; 9: positive for SNP9 E.coli O157:H7; 10: positive for SNP10 E.coli
O157:H7.

223

1

2

3

4

5

6

7 8

9

10

Kd
260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 27. Western blot 8, where 1: ladder; 2: positive for SNP11 E.coli
O157:H7; 3: positive for SNP12 E.coli O157:H7; 4: positive for SNP13 E.coli
O157:H7; 5: positive for SNP14 E.coli O157:H7; 6: positive for SNP15 E.coli
O157:H7; 7: positive for SNP16 E.coli O157:H7; 8: negative for 4-22-10 (BSL
#1); 9: positive for BSL #2 Bio Systems; 10: positive for E. coli ATCC
43895(107).

224

Kd

1

2

3

4

5

6

7 8

9

10

260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 28. Western blot 9, where 1: ladder, 2: positive for Citrobacter freundii CF3GT5742;
3: negative for Enterobacter agglomerans GT1611; 4: positive for E.coli O157:H43 GT 4136; 5:
negative for E.coli O157:H19 164; 6: positive for E.coli O157:H7 GT632; 7: negative for E.coli
O157:H7 GT127; 8: positive for E.coli O157:H7 A110; 9: negative for E.coli O157:H7 125; 10:
positive for E.coli O157:NM GT4141.

225

Kd

1

2

3

4

5

6

7

8

9

10

260
160
110
80
60
50
40
30
20
15
10
3.5

Figure B- 29. Western blot 10, where 1: ladder; 2: positive for E.coli O157:H16 GT4137;
3: positive for E.coli O157:H38 GT4138; 4: negative for E. aerogenes GT47; 5: negative
for E. cloacae GT50; 6: negative for C. freundii GT9173; 7: positive for E.coli O157:H7
GT4135; 8: positive for E.coli O157:H7 GT4132; 9: positive for E.coli O157:H45; 10:
negative for C. freundii GT 4885.

226

Table B- 11. Data for Figures 5-1 and 5-2, passive adsorption.

Controls
Neg (blank)
No antibody
with
PicoGreen
cells
FITC
1 ug/ml
10 ug/ml
100 ug/ml
PicoGreen Cells
1 ug/ml
10 ug/ml
100 ug/ml

Fluorescent Output (RFU)
54.00
27.00
25.00

Average
Fluorescent
Output (RFU)
35.33

Standard
Deviation
16.20

64.00

134.00

153.00

117.00

46.87

45.00
93.00
185.00

60.00
147.00
239.00

58.00
188.00
403.00

54.33
142.67
275.67

8.14
47.65
113.53

55.00
207.00
965.00

103.00
243.00
285.00

91.00
188.00
217.00

83.00
212.67
489.00

24.98
27.93
413.63

227

Table B- 12. Data for Figures 5-1 and 5-2, glutaraldehyde immobilization.

Controls
Fluorescent Output (RFU)
Neg (blank)
38.00
157.00
18.00
No antibody
with
PicoGreen
cells
271.00 144.00 136.00
FITC
1 ug/ml
31
61
36
10 ug/ml
129
81
60
100 ug/ml
100
146
134
PicoGreen Cells
1 ug/ml
123
94
183
10 ug/ml
267
471
255
100 ug/ml
361
925
286

228

Average
Fluorescent Output Standard
(RFU)
Deviation
71.00
75.15

183.67

75.74

42.67
90.00
126.67

16.07
35.37
23.86

133.33
331.00
524.00

45.39
121.39
349.30

Table B- 13. Data for Figures 5-1 and 5-2, EDC / sulfo-NHS immobilization.

Controls
Fluorescent Output (RFU)
Neg (blank)
61.00
31.00
128.00
No antibody
with
PicoGreen
cells
65.00
59.00
98.00
FITC
1 ug/ml
78
52
91
10 ug/ml
272
186
248
100 ug/ml
458
168
170
PicoGreen Cells
1 ug/ml
67
53
63
10 ug/ml
159
171
202
100 ug/ml
213
152
195

229

Average
Fluorescent Output Standard
(RFU)
Deviation
73.33
49.66

74.00

21.00

73.67
235.33
265.33

19.86
44.38
166.86

61.00
177.33
186.67

7.21
22.19
31.34

Table B- 14. Data for Figures 5-5, and 5-6 (0 washes).

0
Washes Fluorescent Output (RFU)
No Cells
No
Block
21
10
15
BSA
Block
20
15
17
Goat
Block
17
8
19
E. coli O157:H7
No
123
249
Block
205
BSA
Block
143
251
273
Goat
Block
84
179
192
Salmonella Enteritidis
No
Block
286
355
737
BSA
Block
1052
350
446
Goat
309
253
Block
296

Average
Fluorescent
Output (RFU)

Standard
Deviation

15.33

5.507570547

17.33

2.516611478

14.67

5.859465277

192.33

63.94789546

222.33

69.57969052

151.67

58.96043871

459.33

242.9286589

616.00

380.6258005

286.00

29.30870178

230

Table B- 15. Data for Figures 5-4, 5-5, and 5-6 (1 wash). Outlier removed from No Cells, Goat
Block using Dixon’s Q-test.
Average
Fluorescent
Standard
Output (RFU)
Deviation
1 Wash Fluorescent Output (RFU)
No Cells
No
Block
17
7
15
13.00
5.291502622
BSA
Block
3
22
10
11.67
9.609023537
Goat
Block
18
18
18.00
0
E. coli O157:H7
No
541
312
412.67
116.9800553
Block
385
BSA
Block
134
196
220
183.33
44.37717131
Goat
Block
338
280
482
366.67
104.0064101
Salmonella Enteritidis
No
Block
126
77
100
101.00
24.51530134
BSA
Block
113
72
83
89.33
21.22105872
Goat
58
84
69.33
13.31665624
Block
66

231

Table B- 16. Data for Figures 5-5 and 5-6 (3 washes). Outliers removed from E. coli O157:H7,
BSA Block and Salmonella Enteritidis, Goat Block using Dixon’s Q-test.
Average
Fluorescent
Standard
3
Output (RFU)
Deviation
Washes Fluorescent Output (RFU)
No Cells
No
Block
18
13
12
14.33
3.214550254
BSA
Block
24
60
14
32.67
24.19366308
Goat
Block
25
19
22
22.00
3
E. coli O157:H7
No
Block
58
112
350
173.33
155.361943
BSA
Block
198
199
198.50
0.707106781
Goat
Block
237
277
165
226.33
56.75679108
Salmonella Enteritidis
No
52
56
52.00
4
Block
48
BSA
121
104
100.67
22.18858565
Block
77
Goat
Block
34
34
34.00
0

232

B.4 CHAPTER 6 DATA
Table B- 17. Data for Figure 6-4.
Voltage
(V)
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0

-2444.66
-2093.41
-1787.00
-1496.54
-1222.04
-964.66
-730.54
-518.18
-329.61
-159.68
-0.36
160.33
330.09
514.78
718.40
945.44
1188.38
1440.76
1684.90
1921.66
2160.11

Current (µA)
-3362.97
-2952.59
-2557.86
-2178.66
-1808.82
-1457.71
-1128.21
-817.14
-523.07
-250.84
0.75
239.27
472.28
698.13
906.79
1100.89
1291.35
1463.18
1605.70
1713.63
1808.86

-3706.54
-3299.89
-2888.70
-2495.20
-2115.82
-1746.82
-1383.73
-1028.77
-680.64
-337.93
0.86
339.79
679.25
1021.93
1366.07
1715.50
2066.07
2421.98
2780.21
3137.72
3486.52

233

Average Current
(µA)
-3171.39
-2781.96
-2411.18
-2056.80
-1715.56
-1389.73
-1080.83
-788.03
-511.11
-249.48
0.41
246.46
493.87
744.95
997.09
1253.94
1515.27
1775.31
2023.60
2257.67
2485.16

Table B- 18. Data for Figures 6-5 and 6-6.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
97
74
80
D7
12
15
5
D8
0
0
0
SPC=(ΣD6)/(n*10^-7)
SPC = 8.4 x 10^8 cfu/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
8.37E+08
8.37E+05
8.37E+03
8.37E+00

234

Table B- 19. Data for Figure 6-5, RT.
Resistance (k )
Time
(min)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

Blank
1.130
2.293
8.597
5.726
5.765
5.784
5.780
5.786
5.790
5.828
5.839
5.896
5.920
5.936
5.936
5.966
5.969
5.979
5.975
5.989
6.041
6.070
6.045
6.045
6.054
6.075
6.079
6.112
6.107
6.102

D8
1.083
2.225
9.085
6.063
6.177
6.111
6.032
6.073
6.031
5.994
5.973
5.963
5.960
5.959
5.987
6.018
6.036
6.056
6.056
6.057
6.061
6.129
6.169
6.187
6.180
6.213
6.241
6.272
6.294
6.260

D5
1.250
2.519
9.327
6.332
6.302
6.319
6.331
6.345
6.368
6.394
6.429
6.468
6.514
6.550
6.580
6.586
6.590
6.587
6.609
6.636
6.660
6.660
6.666
6.677
6.697
6.706
6.733
6.757
6.768
6.762

235

D3
1.406
2.775
9.566
6.924
6.964
6.878
6.796
6.884
6.919
6.948
6.979
7.027
7.056
7.069
7.099
7.125
7.134
7.143
7.169
7.165
7.188
7.221
7.265
7.260
7.257
7.275
7.318
7.349
7.346
7.360

D0
1.547
3.068
10.440
7.264
7.286
7.297
7.327
7.354
7.390
7.426
7.475
7.522
7.542
7.541
7.550
7.556
7.559
7.590
7.619
7.610
7.583
7.583
7.583
7.584
7.598
7.647
7.678
7.690
7.685
7.648

Table B- 20. Data for Figure 6-6, R2.

Time (min)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

Blank
61.538
247.814
314.607
806.916
1040.892
2413.793
35000.011
8000.000
7531.943
9180.329
26987.946
25454.542
15336.163
14997.324
13262.283
11049.724
10949.262
10538.201
11342.001
10930.668
10000.714
9818.015
11083.403
13634.860
8833.476
8188.271
8224.408
10439.093
10012.516
10767.160

Resistance (k
D8
D5
100.000
400.000
400.029
13970.664
442.513
636.364
858.896
771.882
1445.161
866.873
3636.364
1341.317
7417.219
3414.633
5957.447
8549.619
7671.233
8247.423
13023.255 9003.215
67469.882 22857.143
13088.700 42424.258
17041.349 22489.963
16036.656 28806.589
13757.100 22012.574
11904.762 15730.334
11879.193 13371.537
11970.927 14470.285
11377.949 15256.359
206871.356 15397.516
2337.678 15506.078
1826.146 15512.467
1948.755 14762.335
8596.374 14352.091
6395.541 14294.585
20203.661 15247.431
13930.347 15156.108
13402.257 15437.245
13559.322 16220.600
12578.616 15255.530

236

)
D3
D0
80.000
200.000
297.906
1272.988
411.765
560.000
742.706
742.706
1040.892
1040.892
2413.793
2413.793
35000.011 35000.011
8000.000
8000.000
7593.220
7624.234
9003.215
8917.197
20180.177 17919.997
57142.857 200000.000
27654.317 37966.110
32825.329 36842.101
25431.419 24496.943
17857.143 17623.916
15611.062 15642.456
15948.963 16149.965
15938.068 15952.144
15727.687 16009.606
16711.431 17018.693
15258.854 16986.171
15295.530 18036.009
15242.573 18919.557
17498.493 20053.141
16923.234 18673.857
17398.463 18390.561
16903.925 17990.666
16981.533 17590.150
19846.192 17901.096

Table B- 21. Calculated values of R1.
Resistance (k )
Time
(min)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

Blank
1.151
2.314
8.838
5.767
5.797
5.798
5.781
5.790
5.794
5.832
5.841
5.898
5.922
5.938
5.938
5.970
5.973
5.982
5.979
5.993
6.044
6.074
6.048
6.048
6.058
6.079
6.083
6.115
6.111
6.106

D8
1.094
2.238
9.275
6.106
6.203
6.121
6.037
6.080
6.035
5.996
5.974
5.965
5.962
5.962
5.990
6.021
6.039
6.059
6.059
6.057
6.077
6.150
6.188
6.192
6.186
6.215
6.244
6.275
6.297
6.263

D5
1.254
2.519
9.466
6.384
6.348
6.349
6.343
6.349
6.373
6.399
6.431
6.469
6.516
6.551
6.582
6.588
6.594
6.590
6.611
6.638
6.663
6.663
6.669
6.680
6.700
6.709
6.736
6.760
6.771
6.765

D3
1.431
2.801
9.794
6.989
7.011
6.897
6.797
6.890
6.926
6.954
6.981
7.028
7.058
7.070
7.101
7.128
7.137
7.146
7.173
7.168
7.191
7.224
7.268
7.264
7.260
7.278
7.321
7.352
7.349
7.362

237

D0
1.559
3.075
10.638
7.336
7.337
7.319
7.328
7.361
7.398
7.432
7.478
7.522
7.543
7.542
7.553
7.560
7.562
7.594
7.623
7.613
7.586
7.586
7.587
7.588
7.601
7.650
7.681
7.693
7.688
7.651

Table B- 22. Calculated values of ∆R.
Change in R due to R2, ∆R ( )
Time
(min)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

Blank
D8
D5
D3
D0
21.136 11.847
3.918
25.152 12.065
21.406 12.448
0.454
26.089
7.411
241.516 190.429 138.740 227.526 198.328
40.924 43.109 52.370 65.161 71.752
32.107 26.512 46.153 46.905 51.357
13.893 10.287 29.909 19.653 22.128
0.955
4.909
11.759
1.320
1.534
4.187
6.198
4.712
5.929
6.766
4.454
4.745
4.920
6.311
7.171
3.703
2.760
4.545
5.367
6.189
1.264
0.529
1.809
2.414
3.119
1.366
2.717
0.986
0.864
0.283
2.286
2.085
1.887
1.801
1.498
2.350
2.215
1.490
1.522
1.544
2.658
2.607
1.968
1.982
2.328
3.223
3.043
2.758
2.844
3.241
3.256
3.068
3.250
3.262
3.654
3.394
3.065
3.000
3.201
3.569
3.150
3.225
2.864
3.226
3.641
3.284
0.177
2.861
3.266
3.619
3.651
15.755
2.862
3.093
3.380
3.755
20.639
2.860
3.418
3.387
3.298
19.590
3.012
3.452
3.190
2.681
4.456
3.107
3.460
3.042
4.152
5.977
3.139
3.011
2.880
4.510
1.911
2.951
3.128
3.132
4.496
2.797
2.992
3.079
3.207
3.580
2.937
2.959
3.197
3.289
3.727
2.923
2.825
3.179
3.359
3.460
3.117
2.999
2.730
3.269

238

Table B- 23. Percent change due to R2.
Change Due to R2 (%)
Time
(min)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

B
1.871
0.934
2.809
0.715
0.557
0.240
0.017
0.072
0.077
0.064
0.022
0.023
0.039
0.040
0.045
0.054
0.055
0.057
0.053
0.055
0.060
0.062
0.055
0.044
0.069
0.074
0.074
0.059
0.061
0.057

D8
1.094
0.559
2.096
0.711
0.429
0.168
0.081
0.102
0.079
0.046
0.009
0.046
0.035
0.037
0.044
0.051
0.051
0.051
0.053
0.003
0.260
0.337
0.318
0.072
0.097
0.031
0.045
0.047
0.046
0.050

D5
0.313
0.018
1.487
0.827
0.732
0.473
0.186
0.074
0.077
0.071
0.028
0.015
0.029
0.023
0.030
0.042
0.049
0.046
0.043
0.043
0.043
0.043
0.045
0.047
0.047
0.044
0.044
0.044
0.042
0.044

D3
1.789
0.940
2.378
0.941
0.674
0.286
0.019
0.086
0.091
0.077
0.035
0.012
0.026
0.022
0.028
0.040
0.046
0.045
0.045
0.046
0.043
0.047
0.048
0.048
0.041
0.043
0.042
0.043
0.043
0.037

239

D0
0.780
0.242
1.900
0.988
0.705
0.303
0.021
0.092
0.097
0.083
0.042
0.004
0.020
0.020
0.031
0.043
0.048
0.047
0.048
0.048
0.045
0.045
0.042
0.040
0.038
0.041
0.042
0.043
0.044
0.043

Table B- 24. Student’s T-Test comparing RT and R1 (2 tails, α = 0.05).

RT
Blank
D8
D5
D3
D0

R1
Blank
D8
D5
D3
D0

P
Value
0.962
0.967
0.973
0.964
0.969

Significant
Difference
No
No
No
No
No

240

Table B- 25. Resistances of blank sample.
Time (s)
0
30
60
90
120
150
180
210
240
270
300
330
360
390
420
450
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
Average
Total
Average
Total
StDev

Resistance ( )
1694.92 2056.56 5128.20 1951.22
3604.15 4165.53 8004.71 4172.99
17262.64 15258.85 14644.35 20756.11
10164.63 10151.09 12864.69 11739.55
10116.70 10081.73 12690.93 11091.30
10069.23 9831.03 12575.22 10576.41
10011.44 9620.34 12441.13 10395.40
10046.46 9581.74 12544.80 10431.41
10072.67 9648.52 12591.12 10403.43
10134.68 9657.09 12654.79 10306.43
10181.85 9663.90 12671.40 10204.68
10265.75 9657.17 12740.07 10122.66
10266.11 9652.34 12860.14 10050.25
10278.15 9648.12 13016.17 9903.86
10268.71 9644.34 13085.27 9752.12
10276.64 9647.46 13134.99 9646.66
10278.25 9623.17 13171.08 9602.46
10286.89 9543.61 13329.46 9540.16
10306.13 9462.33 13552.30 9533.73
10338.28 9439.37 13669.34 9537.24
10352.39 9453.39 13676.68 9505.22
10359.24 9457.94 13708.76 9412.14
10411.55 9454.35 13914.84 9367.68
10466.20 9450.28 14046.64 9336.90
10487.10 9443.19 14088.48 9318.43
10468.46 9431.82 14173.77 9290.23
10450.78 9430.47 14333.84 9246.32
10438.48 9438.41 14495.23 9207.92
10432.65 9447.01 14543.49 9187.57
10426.17 9445.10 14582.57 9190.12
10427.53 9445.10 14643.97 9241.08
5753.78 10020.80 9352.62 13018.66 9742.63
1129.94
2292.51
8596.87
5726.09
5764.99
5783.93
5780.35
5785.72
5790.03
5828.35
5839.29
5896.23
5919.79
5935.72
5935.60
5966.33
5969.26
5978.82
5975.50
5989.43
6040.60
6070.20
6044.51
6044.90
6053.92
6074.94
6078.63
6111.80
6106.87
6102.21

9602.53
3102.71

241

Table B- 26. Data for Figures 6-7, 6-8, and 6-12 experiment A.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
134
152
130
D7
12
15
10
D8
0
0
0
SPC=(ΣD6)/(n*10^-7)
SPC = 1.39E+09 CFU/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
1.39E+09
1.39E+06
1.39E+04
1.39E+01

242

Table B- 27. Data for Figure 6-7, 6-8, and 6-12, experiment A resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
420
450
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
420
450
481
511

Blank
9620.34
9581.74
9648.52
9657.09
9663.90
9657.17
9652.34
9648.12
9644.34
9647.46
9623.17
9543.61
9462.33
9439.37
9453.39
9457.94
9454.35
9450.28
9443.19
9431.82
9430.47
9438.41
9447.01
9445.10
9445.10
12441.13
12544.80
12591.12
12654.79
12671.40
12740.07
12860.14
13016.17
13085.27
13134.99
13171.08
13329.46

D8
10513.67
10546.14
10508.54
10469.44
10533.25
10547.81
10543.95
10559.62
10572.30
10591.18
10642.50
10697.35
10659.47
10619.66
10563.99
10518.05
10512.36
10549.20
10590.74
10644.04
10687.23
10669.27
10608.87
10559.66
10554.09
15973.53
15961.69
16340.73
16612.58
16986.58
17344.12
17612.06
17794.39
18002.73
18450.31
18911.76
19177.68

D5
10729.61
10728.38
10803.93
10822.63
10916.22
10904.22
10917.87
10916.34
10942.58
10915.80
10908.68
10984.61
11096.67
11100.63
11020.59
10985.46
10979.47
10990.65
11002.01
11002.80
11028.86
10996.88
11006.67
11046.67
10966.42
25132.40
25152.15
25640.55
26042.15
26218.70
26192.22
26422.56
26479.29
26465.78
26549.09
26945.62
27414.70

D3
11316.08
11425.10
11459.82
11505.97
11474.05
11495.46
11470.10
11553.30
11626.89
11725.83
11767.08
11784.71
11788.26
11769.40
11786.62
11782.87
11784.83
11779.80
11769.11
11755.57
11762.64
11776.58
11781.35
11778.56
11787.24
30663.94
31218.64
31361.21
31267.72
31186.30
31192.24
31188.38
31240.78
31286.66
31333.48
31281.42
31314.31

243

D0
11860.38
11922.50
11927.38
11919.15
11850.70
11745.07
11718.86
11770.84
11848.83
11848.84
11870.28
11860.95
11885.48
11841.59
11786.59
11705.39
11686.92
11713.47
11764.36
11796.03
11820.13
11813.60
11834.82
11945.65
12208.15
32149.25
32825.33
33023.53
32777.66
32853.04
32776.14
33248.62
33706.51
33785.61
33797.65
33825.77
33836.25

Table B-27 (cont’d).
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
420
450
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901

13552.30
13669.34
13676.68
13708.76
13914.84
14046.64
14088.48
14173.77
14333.84
14495.23
14543.49
14582.57
14643.97
10395.40
10431.41
10403.43
10306.43
10204.68
10122.66
10050.25
9903.86
9752.12
9646.66
9602.46
9540.16
9533.73
9537.24
9505.22
9412.14
9367.68
9336.90
9318.43
9290.23
9246.32
9207.92
9187.57
9190.12
9241.08

19374.62
19728.38
20118.41
20542.55
20527.10
21068.40
21427.69
21901.34
21786.15
22083.41
22243.41
22421.52
22714.37
11234.15
11267.61
11261.53
11125.06
10904.37
10846.44
10863.87
10849.94
10788.61
10686.53
10467.09
10304.54
10129.44
10015.95
9895.60
9854.92
9805.78
9746.25
9680.31
9647.12
9648.49
9654.21
9661.71
9671.18
9716.99

27723.59
27947.18
28048.81
28203.35
28401.89
28611.12
28662.38
28484.23
28411.96
28443.72
28965.71
29542.10
28766.63
12247.40
12330.80
12372.87
12431.01
12461.75
12475.21
12486.12
12521.69
12516.48
12502.01
12498.27
12513.91
12512.73
12520.07
12512.29
12521.41
12494.48
12488.35
12494.37
12528.52
12555.15
12605.57
12618.24
12588.51
12646.22

31395.42
31647.81
31762.23
31914.74
32068.71
32251.88
32173.19
32004.34
31850.74
31802.06
31867.43
31923.38
31796.50
12731.70
12749.54
12748.95
12711.42
12677.50
12681.91
12705.98
12742.85
12809.15
12917.75
12931.83
12923.42
12967.52
13068.72
13108.37
13200.57
13253.68
13266.43
13270.52
13321.60
13331.94
13349.80
13424.56
13518.08
13603.13

244

33992.70
33865.29
33998.90
34368.16
34781.30
34381.68
33970.69
34142.28
34714.46
35156.01
35199.77
35133.95
35026.27
13837.41
13926.07
13907.24
13887.00
13885.22
13916.70
13938.17
13978.28
14010.02
14073.25
14111.05
14162.66
14191.80
14243.85
14274.79
14315.59
14359.34
14386.90
14405.59
14442.48
14499.96
14539.68
14588.58
14598.54
14458.70

Table B- 28. Data for Figure 6-7, experiment A.
Average Resistance (k )
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

Blank
10.82
10.85
10.88
10.87
10.85
10.84
10.85
10.86
10.83
10.81
10.80
10.80
10.85
10.88
10.88
10.86
10.91
10.94
10.95
10.97
11.00
11.05
11.06
11.07
11.11

D8
12.57
12.59
12.70
12.74
12.81
12.91
13.01
13.07
13.12
13.24
13.34
13.39
13.39
13.45
13.53
13.64
13.62
13.79
13.90
14.06
14.04
14.14
14.17
14.22
14.33

D5
16.04
16.07
16.27
16.43
16.53
16.52
16.61
16.64
16.64
16.66
16.78
16.97
17.11
17.19
17.19
17.24
17.29
17.36
17.39
17.34
17.33
17.35
17.53
17.73
17.46

D3
18.24
18.46
18.52
18.50
18.45
18.46
18.45
18.51
18.57
18.66
18.66
18.67
18.72
18.83
18.89
18.97
19.04
19.10
19.07
19.03
18.98
18.98
19.02
19.07
19.06

245

D0
19.28
19.56
19.62
19.53
19.53
19.48
19.64
19.82
19.88
19.91
19.94
19.95
20.02
19.98
20.02
20.13
20.28
20.16
20.05
20.13
20.34
20.50
20.54
20.56
20.56

Table B- 29. Data for Figure 6-8, experiment A.
Blank
Average Resistance
(k )
Standard Deviation
(k )

D8

D5

D3

D0

10.90

13.43

16.95

18.76

19.98

1.92

4.45

7.50

9.14

9.97

Table B- 30. Student’s T-Test of data, experiment A.
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank
D8

D0
D5

D8

D3

D8
D5
D5
D3

D0
D3
D0
D0

P Value
1.71E05
1.73E09
1.95E10
2.69E11
0.001
1.49E05
1.06E06
0.187
0.037
0.436

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No

246

Table B- 31. Data for Figures 6-9 and 6-12, experiment B.
Sample Plate Counts
Blank
0
0
D6
103
82
D7
14
5
D8
1
1
SPC=(ΣD6)/(n*10^-7)
SPC =
9.10E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

0
88
8
2

Concentration
(CFU/mL)
0.00E+00
9.10E+08
9.10E+05
9.10E+03
9.10E+00

247

Table B- 32. Data for Figures 6-10 and 6-12, experiment C.
Sample Plate Counts
Blank
0
0
D5
TNC
TNC
D6
74
92
D7
4
2
D8
0
2
SPC=(ΣD6)/(n*10^-7)
SPC =
7.30E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

0
354
78
9
0

Concentration
(CFU/mL)
0.00E+00
7.30E+08
7.30E+05
7.30E+03
7.30E+00

248

Table B- 33. Data for Figures 6-11 and 6-12, experiment D.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
80
82
123
D7
4
6
4
D8
0
1
0
SPC=(ΣD6)/(n*10^-7)
SPC =
9.50E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
9.50E+08
9.50E+05
9.50E+03
9.50E+00

249

Table B- 34. Data for Figure 6-12, experiment E.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
97
74
80
D7
12
15
5
D8
0
0
0
SPC=(ΣD6)/(n*10^-7)
SPC =
8.37E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
8.37E+08
8.37E+05
8.37E+03
8.37E+00

250

Table B- 35. Data for Figure 6-12, experiment F.
Sample Plate Counts
Blank
0
0
D5
TNC
TNC
D6
30
37
D7
3
1
D8
0
0
SPC=(ΣD6)/(n*10^-7)
SPC =
3.23E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

0
294
30
5
0

Concentration
(CFU/mL)
0.00E+00
3.23E+08
3.23E+05
3.23E+03
3.23E+00

251

Table B- 36. Data for Figure 6-12, experiment G.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
149
128
141
D7
10
13
19
D8
0
0
0
SPC=(ΣD6)/(n*10^-7)
SPC =
1.39E+09 CFU/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
1.39E+09
1.39E+06
1.39E+04
1.39E+01

252

Table B- 37. Data for Figure 6-12, experiment H.
Sample Plate Counts
Blank
0
0
0
D5
TNC
TNC
TNC
D6
44
73
68
D7
1
19
17
D8
0
0
1
SPC=(ΣD6)/(n*10^-7)
SPC =
6.17E+08 CFU/mL

Sample
Blank
D0
D3
D5
D8

Concentration
(CFU/mL)
0.00E+00
6.17E+08
6.17E+05
6.17E+03
6.17E+00

253

Table B- 38. Data for Figure 6-12, experiment A response.
Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5

D8
9.29
10.06
8.91
8.41
9.00
9.22
9.24
9.45
9.62
9.78
10.59
12.09
12.65
12.50
11.75
11.21
11.19
11.63
12.15
12.85
13.33
13.04
12.30
11.80
11.74
28.39
27.24
29.78
31.27
34.05
36.14
36.95
36.71
37.58
40.47
43.59
43.87

D5
11.53
11.97
11.97
12.07
12.96
12.91
13.11
13.14
13.46
13.15
13.36
15.10
17.27
17.60
16.58
16.15
16.13
16.30
16.51
16.66
16.95
16.51
16.51
16.96
16.11
102.01
100.50
103.64
105.79
106.91
105.59
105.46
103.43
102.26
102.12
104.58
105.67

D3
17.63
19.24
18.77
19.15
18.73
19.04
18.83
19.75
20.56
21.54
22.28
23.48
24.58
24.68
24.68
24.58
24.65
24.65
24.63
24.64
24.73
24.77
24.71
24.71
24.80
146.47
148.86
149.07
147.08
146.12
144.84
142.52
140.02
139.10
138.55
137.50
134.93

D0
23.28
24.43
23.62
23.42
22.63
21.62
21.41
22.00
22.86
22.82
23.35
24.28
25.61
25.45
24.68
23.76
23.61
23.95
24.58
25.07
25.34
25.17
25.28
26.47
29.25
158.41
161.66
162.28
159.01
159.27
157.27
158.54
158.96
158.20
157.31
156.82
153.85
254

Table B-38 (cont’d).
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

42.96
44.33
47.10
49.85
47.52
49.99
52.09
54.52
51.99
52.35
52.94
53.76
55.11
8.07
8.02
8.25
7.94
6.86
7.15
8.10
9.55
10.63
10.78
9.00
8.01
6.25
5.02
4.11
4.70
4.68
4.38
3.88
3.84
4.35
4.85
5.16
5.23
5.15

104.57
104.45
105.08
105.73
104.11
103.69
103.45
100.96
98.22
96.23
99.17
102.58
96.44
17.82
18.21
18.93
20.61
22.12
23.24
24.24
26.43
28.35
29.60
30.16
31.17
31.25
31.28
31.64
33.03
33.38
33.75
34.08
34.86
35.79
36.90
37.34
36.98
36.85

131.66
131.52
132.24
132.81
130.46
129.61
128.37
125.80
122.21
119.40
119.12
118.91
117.13
22.47
22.22
22.55
23.33
24.23
25.28
26.42
28.67
31.35
33.91
34.67
35.46
36.02
37.03
37.91
40.25
41.48
42.09
42.41
43.39
44.19
44.98
46.12
47.09
47.20

150.83
147.75
148.59
150.70
149.96
144.77
141.12
140.88
142.19
142.54
142.03
140.93
139.19
33.11
33.50
33.68
34.74
36.07
37.48
38.68
41.14
43.66
45.89
46.95
48.45
48.86
49.35
50.18
52.10
53.29
54.09
54.59
55.46
56.82
57.90
58.79
58.85
56.46

255

Table B- 39. Experiment A average response.
Average Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

D8
15.25
15.11
15.65
15.88
16.64
17.50
18.09
18.57
19.28
20.34
21.06
21.33
20.62
20.62
20.99
21.92
21.13
22.00
22.71
23.74
23.22
23.41
23.47
23.60
24.00

D5
43.79
43.56
44.85
46.16
47.33
47.25
47.60
47.67
48.02
48.29
49.37
50.65
51.03
51.11
51.10
51.64
51.21
51.25
51.35
50.83
50.32
49.88
51.01
52.17
49.80

D3
62.19
63.44
63.46
63.19
63.03
63.05
62.59
62.81
63.67
64.67
64.82
64.62
64.09
64.41
64.94
65.88
65.53
65.45
65.14
64.61
63.71
63.05
63.31
63.57
63.04

D0
71.60
73.20
73.19
72.39
72.65
72.12
72.88
74.03
74.90
75.34
75.71
75.53
75.10
74.18
74.48
75.52
75.62
74.27
73.43
73.80
74.78
75.20
75.36
75.42
74.97

256

Table B- 40. Data for Figures 6-9 and 6-12, experiment B resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511

B
56611.40
60375.08
62473.50
61201.27
60051.39
61097.35
62218.70
63783.48
65738.93
67097.18
67833.77
68427.86
67760.51
66166.08
64076.15
62058.45
59242.96
58778.46
59473.23
61859.65
62259.58
62584.16
62277.58
63593.00
65897.85
105144.58
110671.94
108573.44
106209.45
106072.66
105668.35
105940.22
108315.96
111390.68
111690.84
111007.21
109995.17

D8
63240.92
63611.78
64043.00
64258.50
64220.37
64171.61
64369.02
64474.16
65236.22
65214.48
65267.01
64712.95
65617.94
64467.11
64152.13
64279.15
66539.92
67556.16
67673.71
67019.31
67020.91
67583.87
68736.96
69930.06
69204.15
89639.44
95076.39
112219.94
111124.32
111843.41
110488.52
112300.95
109982.91
112871.81
116784.17
113784.59
108198.97

D5
72077.86
72141.70
71946.37
71461.38
71138.21
70701.71
68909.51
67464.99
67655.73
70311.13
71837.23
72235.69
70834.06
69684.67
69622.29
70387.57
71076.81
71358.03
71545.37
72338.80
73026.96
73465.75
72920.46
72926.16
71620.41
97357.45
101486.04
103907.68
108208.39
111964.18
114815.26
114034.37
113663.10
113614.70
115095.73
114820.71
114710.37
257

D3
72407.54
73087.96
72101.77
71455.91
73763.80
74274.49
73857.19
73763.80
74740.91
74710.50
74204.12
75315.38
75180.40
75402.59
73898.12
74185.94
74768.35
75934.26
75817.06
75840.16
76032.33
75761.67
75153.29
74557.32
74005.55
115408.59
122132.06
120078.26
117484.16
118645.95
121754.22
123738.30
122654.57
123478.83
126164.82
129732.61
131311.83

D0
70493.45
70818.84
71583.32
71753.47
71896.14
71922.58
72632.35
72475.49
73258.52
73239.00
74349.44
74074.07
73999.68
73094.64
73240.91
73197.83
73089.39
73437.80
74664.18
75522.59
75977.01
75874.58
76072.48
75400.57
72793.45
120911.15
125201.20
127278.51
130861.00
130584.82
127146.81
124747.95
124944.99
123874.35
120152.68
117571.65
117512.32

Table B-40 (cont’d).
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901

110195.17
110068.64
107566.48
104327.74
103488.77
104113.11
105961.54
110043.83
111226.74
112196.55
113616.07
115025.16
111888.11
98039.22
116959.06
126748.46
123527.61
118363.20
114355.71
109430.55
103941.22
101190.42
101415.09
101935.59
101898.95
103156.87
102375.10
102584.68
105004.21
108624.39
109310.95
109347.65
114339.37
117676.21
115942.51
112562.80
109394.23
95192.77

103484.08
104152.13
111013.32
115348.80
114768.08
109922.46
103181.58
98802.01
97721.85
98420.00
97623.47
97276.26
93984.96
123261.12
126949.56
128058.55
129425.91
130162.91
131381.36
131935.45
133590.33
137803.42
139981.80
143190.29
142966.53
142429.12
143763.72
151032.94
156137.14
154908.35
154301.84
153454.91
151789.89
148199.13
145013.83
143342.01
143292.14
145586.90

114212.72
114426.50
115251.80
116191.93
115998.65
115074.93
114163.51
113838.53
113527.51
113282.79
113304.23
113010.31
111529.35
116424.11
125673.26
125532.38
126559.40
129557.64
135235.47
137083.54
136888.64
137085.04
140506.47
142830.37
142045.78
142142.77
148668.07
154773.94
155832.61
154694.64
154340.57
154342.95
153886.50
154703.88
152378.26
150248.92
148011.10
148478.10

258

131053.63
129634.43
129134.30
128529.98
127052.47
127213.61
127729.65
130407.78
127849.80
129403.34
126081.24
121506.68
127327.71
173455.12
152061.28
157764.24
157691.80
156853.89
156772.15
156612.90
156179.48
154795.17
154094.93
154306.08
156138.90
155491.64
157669.62
159376.37
160020.12
156727.73
153555.06
152233.69
155829.99
157536.13
158485.79
158317.28
159140.64
157511.32

119070.38
120897.44
121190.09
121960.65
120585.69
117920.55
119834.27
122970.98
127479.57
127477.85
128018.12
123342.58
120992.14
143076.13
158640.21
164790.69
165172.24
163300.98
163905.62
164858.61
167218.10
168522.15
167023.60
159556.66
155078.24
156109.11
159567.00
158587.58
157411.35
155681.37
153924.11
152576.72
153173.18
152382.48
152198.52
154281.04
159077.35
160610.32

Table B- 41. Data for Figures 6-9 and 6-12, experiment B response.
Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5

D8
11.71
5.36
2.51
5.00
6.94
5.03
3.46
1.08
-0.76
-2.81
-3.78
-5.43
-3.16
-2.57
0.12
3.58
12.32
14.93
13.79
8.34
7.65
7.99
10.37
9.97
5.02
-14.75
-14.09
3.36
4.63
5.44
4.56
6.00
1.54
1.33
4.56
2.50
-1.63

D5
27.32
19.49
15.16
16.76
18.46
15.72
10.75
5.77
2.92
4.79
5.90
5.56
4.54
5.32
8.66
13.42
19.98
21.40
20.30
16.94
17.29
17.39
17.09
14.68
8.68
-7.41
-8.30
-4.30
1.88
5.55
8.66
7.64
4.94
2.00
3.05
3.44
4.29

D3
27.90
21.06
15.41
16.76
22.83
21.57
18.71
15.65
13.69
11.35
9.39
10.07
10.95
13.96
15.33
19.54
26.21
29.19
27.48
22.60
22.12
21.06
20.67
17.24
12.30
9.76
10.36
10.60
10.62
11.85
15.22
16.80
13.24
10.85
12.96
16.87
19.38

D0
24.52
17.30
14.58
17.24
19.72
17.72
16.74
13.63
11.44
9.15
9.61
8.25
9.21
10.47
14.30
17.95
23.37
24.94
25.54
22.09
22.03
21.24
22.15
18.57
10.46
15.00
13.13
17.23
23.21
23.11
20.33
17.75
15.35
11.21
7.58
5.91
6.83
259

Table B-41 (cont’d).
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

-6.09
-5.38
3.20
10.56
10.90
5.58
-2.62
-10.22
-12.14
-12.28
-14.08
-15.43
-16.00
25.73
8.54
1.03
4.77
9.97
14.89
20.57
28.52
36.18
38.03
40.47
40.30
38.07
40.43
47.23
48.70
42.61
41.16
40.34
32.75
25.94
25.07
27.34
30.99
52.94

3.65
3.96
7.14
11.37
12.09
10.53
7.74
3.45
2.07
0.97
-0.27
-1.75
-0.32
18.75
7.45
-0.96
2.45
9.46
18.26
25.27
31.70
35.47
38.55
40.12
39.40
37.79
45.22
50.87
48.41
42.41
41.19
41.15
34.59
31.47
31.43
33.48
35.30
55.98

18.93
17.78
20.05
23.20
22.77
22.19
20.54
18.51
14.95
15.34
10.97
5.63
13.80
76.92
30.01
24.47
27.66
32.52
37.09
43.12
50.26
52.97
51.94
51.38
53.23
50.73
54.01
55.36
52.39
44.28
40.48
39.22
36.29
33.87
36.69
40.65
45.47
65.47

8.05
9.84
12.67
16.90
16.52
13.26
13.09
11.75
14.61
13.62
12.68
7.23
8.14
45.94
35.64
30.01
33.71
37.97
43.33
50.65
60.88
66.54
64.69
56.53
52.19
51.33
55.87
54.59
49.91
43.32
40.81
39.53
33.96
29.49
31.27
37.06
45.42
68.72

260

Table B- 42. Data for Figure 6-9, experiment B average response.
Average Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

D8
7.56
-0.06
2.30
4.80
7.45
8.16
10.01
10.38
12.25
13.26
13.06
11.08
9.61
10.83
16.85
20.95
21.94
20.56
17.17
10.29
7.15
6.93
7.88
8.51
13.99

D5
12.89
6.21
3.30
7.03
11.16
14.21
14.55
14.14
13.46
15.46
16.49
16.42
15.32
18.17
22.22
24.40
24.83
24.37
23.06
18.33
16.94
16.59
16.77
16.08
21.45

D3
38.20
20.47
16.83
18.34
22.40
24.63
26.21
26.38
25.84
25.42
25.88
27.56
26.87
28.58
30.25
31.71
31.09
30.62
29.08
25.80
23.65
24.36
24.10
22.78
30.52

D0
28.48
22.02
20.61
24.72
26.93
27.12
28.38
29.95
29.73
27.14
24.02
22.42
22.86
25.39
27.19
28.25
27.74
26.34
26.06
22.60
22.05
22.04
23.96
23.74
29.11

261

Table B- 43. Data for Figures 6-10 and 6-12, experiment C resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511

B
230234.80
249465.40
253450.99
251188.62
250682.61
254014.32
268430.58
273029.49
267958.01
259901.74
255925.18
255685.55
255964.62
258535.28
259723.39
259842.98
258892.04
258110.48
257991.81
256944.17
253808.91
247581.87
243769.97
239808.15
225035.16
156774.90
170212.77
170150.67
170212.77
171568.61
171442.54
172159.35
174064.40
181729.70
184746.51
183653.51
178732.16

D8
288065.89
249276.60
258350.27
267660.79
280168.08
284466.05
287326.76
287654.56
289384.68
285297.91
284645.32
285051.18
288565.31
292562.59
296143.44
300360.73
303142.74
303606.79
304328.77
308038.26
309629.16
309812.19
312996.10
317965.02
303260.05
276679.82
245022.97
259499.50
256292.89
250738.80
247209.99
248517.72
248857.87
248459.30
248948.57
250677.08
251523.10

D5
324825.93
348085.43
344870.16
329994.01
317784.56
312395.35
313725.49
307709.16
300903.73
299587.05
309741.37
319671.22
325570.01
324739.43
324224.15
313518.15
303152.48
299737.73
311964.59
326043.86
329075.97
327217.42
317485.51
311041.99
335289.19
291666.62
255009.14
268894.69
273838.61
277083.68
275863.74
270492.22
264515.42
262613.32
265397.57
266606.66
267415.57
262

D3
514705.87
451248.88
482259.60
442897.65
379044.31
337874.59
338425.74
350811.22
360188.77
366727.27
368703.56
369599.41
365778.90
361746.08
359461.38
363273.88
363875.86
366319.96
362112.59
363579.78
356293.10
342026.51
334220.58
346921.08
342319.21
288065.89
249944.14
257122.55
258207.25
262448.73
262738.07
265787.09
271396.64
278139.20
276629.31
273826.16
272111.51

D0
322878.32
314421.31
321950.04
319589.09
312849.22
312421.50
308437.87
308856.46
305187.14
310067.14
314148.69
319711.29
315871.13
314677.37
313598.97
315522.11
315802.50
312329.19
306760.70
305710.55
310113.10
315164.12
321137.80
324280.50
321285.14
511883.01
414354.22
404916.99
372389.83
357644.58
347826.09
361205.12
359642.89
350467.45
345543.46
353394.60
353072.93

Table B-43 (cont’d).
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901

179340.03
185442.74
188959.33
191859.70
193713.98
201567.88
204400.41
208130.49
209111.23
212180.70
216585.79
222407.56
224466.89
230263.11
250000.00
250284.95
249203.17
247694.09
245334.23
242893.31
241227.99
238319.03
234447.46
230249.75
228435.36
225635.23
224180.77
222734.81
222603.42
221130.62
218185.31
215114.92
216994.96
220290.33
227462.87
237123.37
244349.42
236266.98

252462.65
252642.00
252111.43
254546.07
252807.94
255731.06
262646.29
268407.52
265197.12
261873.75
256107.52
248601.62
259571.71
230078.64
251004.27
250514.43
251127.06
249669.81
250040.26
248629.92
248902.71
248569.11
248823.44
248095.13
246346.19
244733.46
244163.04
245055.15
248119.13
250936.49
251628.79
249473.74
249859.45
249440.38
254478.31
256163.29
255183.41
264375.41

270452.57
273237.33
279611.09
280322.33
281874.51
281291.10
283062.72
276313.93
275528.24
280316.67
285839.73
283185.84
272944.39
282258.00
247459.09
258135.93
257566.00
259091.39
256433.80
255777.89
261211.05
267599.48
269837.95
272143.94
275511.85
274957.93
270560.14
266437.60
263115.80
263920.63
264647.72
267220.20
270375.35
271313.43
269970.53
268293.17
266577.81
265780.73

263

270983.62
273170.31
271944.27
272734.31
273020.99
276960.13
273399.59
275812.00
282696.86
305767.01
315891.69
308522.95
314341.85
237690.94
257352.96
260368.25
263653.42
266641.21
268456.38
272638.69
273715.54
271091.90
268801.74
265919.54
264218.24
260479.62
257753.35
256201.49
260029.74
262074.15
261474.55
260138.37
261376.95
263765.25
271123.36
273552.31
268817.20
277777.78

348158.87
348822.69
364251.22
369515.01
370149.93
367623.00
369754.11
365211.15
358047.18
350644.41
360133.25
372266.17
354139.00
242005.12
263653.42
267181.94
267713.53
267865.70
267291.06
266514.72
266736.52
267659.79
268775.88
271817.87
277118.54
278302.38
273123.45
267668.53
272668.57
271823.48
270560.72
271217.92
274919.53
270769.51
268752.76
270900.52
272294.08
271554.65

Table B- 44. Data for Figures 6-10 and 6-12, experiment C response.
Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5

D8
25.12
-0.08
1.93
6.56
11.76
11.99
7.04
5.36
8.00
9.77
11.22
11.49
12.74
13.16
14.02
15.59
17.09
17.63
17.96
19.89
21.99
25.14
28.40
32.59
34.76
76.48
43.95
52.51
50.57
46.14
44.19
44.35
42.97
36.72
34.75
36.49
40.73

D5
41.08
39.53
36.07
31.37
26.77
22.98
16.87
12.70
12.30
15.27
21.03
25.03
27.19
25.61
24.83
20.66
17.10
16.13
20.92
26.89
29.66
32.17
30.24
29.70
48.99
86.04
49.82
58.03
60.88
61.50
60.91
57.12
51.96
44.51
43.65
45.17
49.62

D3
123.56
80.89
90.28
76.32
51.20
33.01
26.08
28.49
34.42
41.10
44.07
44.55
42.90
39.92
38.40
39.81
40.55
41.92
40.36
41.50
40.38
38.15
37.10
44.67
52.12
83.74
46.84
51.11
51.70
52.97
53.25
54.38
55.92
53.05
49.73
49.10
52.25

D0
40.24
26.04
27.03
27.23
24.80
22.99
14.90
13.12
13.89
19.30
22.75
25.04
23.40
21.72
20.74
21.43
21.98
21.01
18.90
18.98
22.18
27.30
31.74
35.22
42.77
226.51
143.43
137.98
118.78
108.46
102.88
109.81
106.61
92.85
87.04
92.42
97.54
264

Table B-44 (cont’d).
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

40.77
36.24
33.42
32.67
30.51
26.87
28.50
28.96
26.82
23.42
18.25
11.78
15.64
-0.08
0.40
0.09
0.77
0.80
1.92
2.36
3.18
4.30
6.13
7.75
7.84
8.46
8.91
10.02
11.46
13.48
15.33
15.97
15.15
13.23
11.88
8.03
4.43
11.90

50.80
47.34
47.97
46.11
45.51
39.55
38.48
32.76
31.76
32.11
31.98
27.33
21.60
22.58
-1.02
3.14
3.36
4.60
4.52
5.30
8.28
12.29
15.10
18.20
20.61
21.86
20.69
19.62
18.20
19.35
21.29
24.22
24.60
23.16
18.69
13.14
9.10
12.49

51.10
47.31
43.92
42.15
40.94
37.40
33.76
32.52
35.19
44.11
45.85
38.72
40.04
3.23
2.94
4.03
5.80
7.65
9.42
12.25
13.47
13.75
14.65
15.49
15.66
15.44
14.98
15.03
16.81
18.52
19.84
20.93
20.45
19.74
19.19
15.36
10.01
17.57

94.13
88.10
92.77
92.60
91.08
82.38
80.90
75.47
71.22
65.26
66.28
67.38
57.77
5.10
5.46
6.75
7.43
8.14
8.95
9.73
10.57
12.31
14.64
18.05
21.31
23.34
21.83
20.17
22.49
22.92
24.01
26.08
26.69
22.91
18.15
14.24
11.44
14.94

265

Table B- 45. Data for Figure 6-10, experiment C average response.
Average Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

D8
33.84
14.76
18.18
19.30
19.57
19.37
17.92
17.17
16.34
16.88
18.49
20.02
20.66
19.44
19.15
19.91
20.36
19.94
20.81
21.33
20.68
20.14
18.23
16.27
20.77

D5
49.90
29.44
32.41
31.87
30.96
29.47
26.43
24.32
23.03
24.67
28.13
31.75
33.29
31.21
30.81
28.32
27.32
25.66
27.88
28.08
28.19
27.66
25.12
22.04
27.69

D3
70.18
43.56
48.47
44.61
37.27
31.90
30.90
32.62
33.74
35.16
36.22
37.49
36.48
34.07
32.45
32.92
33.34
33.06
31.68
31.49
31.77
33.82
32.77
31.13
36.58

D0
90.62
58.31
57.25
51.15
47.13
44.94
44.81
43.44
39.69
40.33
44.41
47.97
46.96
43.88
44.56
45.51
45.33
42.46
41.96
40.38
38.77
36.90
37.42
38.01
38.49

266

Table B- 46. Data for Figures 6-11 and 6-12, experiment D resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511

B
230263.11
250000.00
250738.80
249066.00
239952.01
231684.96
229819.02
233248.23
234867.82
240141.82
249472.12
251274.34
251114.32
248204.98
250324.04
247732.75
250929.82
250367.16
251671.86
256342.82
258119.27
260664.57
260525.63
261780.10
269632.63
68176.28
67323.87
66916.80
66111.02
67123.74
68119.88
68884.07
68946.83
68487.29
68261.05
68744.34
69097.78

D8
237690.94
258189.38
257494.90
257177.17
258702.36
265096.59
269224.23
269146.98
266666.67
267309.49
267724.86
267324.78
268964.41
273496.19
277485.96
277227.02
275685.33
274540.49
275167.96
276228.75
276553.72
273517.14
270849.60
271831.46
277874.26
81693.67
82403.22
81554.19
79997.13
78431.37
77615.54
78330.45
79986.28
81063.08
81081.87
80309.76
80054.89

D5
301724.11
264525.21
275130.11
277502.43
283659.13
292703.37
297086.94
296648.23
295184.30
294884.00
295233.63
300596.93
300261.17
300948.29
297501.99
297833.56
295358.67
297864.96
299911.95
303616.56
306246.07
304463.50
304291.55
312012.48
319233.84
82171.67
84058.83
84500.24
86148.54
87502.72
87456.27
87089.03
86775.95
86483.81
86943.02
87958.34
88397.79
267

D3
307017.44
334108.91
331674.96
325108.94
317460.32
317154.64
315777.57
313188.62
309837.34
313641.07
321086.25
327479.53
327034.00
324253.79
322567.53
323098.02
321784.07
324289.34
320456.33
316243.33
318087.81
320336.84
324923.87
335008.38
332640.33
95398.31
98606.30
97703.96
95665.16
94051.26
93502.41
93448.58
93679.94
93802.33
94349.15
94527.54
94984.48

D0
507246.37
417443.11
413528.33
372241.50
359827.69
344466.96
351169.71
349493.20
347820.04
348178.50
350047.64
351550.44
347887.56
341983.46
337304.30
335051.25
329764.62
325297.61
328027.60
339225.45
347583.08
350433.56
356818.66
365965.23
347222.22
97582.22
100000.00
100034.83
99978.58
99356.84
99083.48
99582.99
100150.23
100086.68
100239.85
101069.35
101852.43

Table B-46 (cont’d).
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901

69055.70
69002.06
69108.27
69250.57
69401.44
69329.23
69181.92
69456.50
70598.32
72181.68
73101.32
73193.05
73800.74
115798.18
126182.97
126446.52
126915.05
124951.18
123587.58
123874.48
125513.98
126271.97
126735.26
127310.01
127878.25
128486.58
128252.09
128089.16
127788.70
127950.28
127351.77
126794.78
126946.31
129219.07
129847.23
130678.54
132868.29
133489.07

80286.73
80894.46
81046.66
82544.73
85003.03
86719.51
86518.54
86024.14
85973.95
86951.12
87170.38
86299.89
87145.97
149492.79
131009.47
136181.78
135830.00
137837.65
140042.02
140585.17
140009.63
139969.18
142521.73
143712.07
143718.51
143455.10
143570.61
142381.67
141477.01
141209.11
140885.47
141299.62
143605.95
146148.45
147314.93
146854.44
146439.69
144822.59

88548.05
88835.30
88978.58
89090.40
89109.53
89110.42
89105.99
89935.45
89410.44
89965.79
90573.84
91324.20
91220.07
164997.06
148128.53
160339.00
152905.20
146344.02
140951.40
141614.38
141960.48
142899.40
143185.16
143413.96
143878.60
144177.52
144232.75
144519.49
145440.84
147157.38
148133.14
149439.30
148990.84
149187.73
149892.14
150925.49
150375.94
151601.29

268

95015.09
95457.25
95027.98
95460.51
96715.13
97073.92
96359.00
96765.27
97796.09
97683.49
98022.05
98887.52
97323.60
150375.94
163265.31
164299.93
160375.72
154278.47
150634.81
151884.97
151516.80
149459.27
148619.94
150037.51
151366.90
152246.72
152592.69
153196.32
153359.09
153782.13
153656.05
153565.20
153465.89
154025.58
154100.87
154218.98
154231.73
154469.97

102090.11
101952.58
101705.75
101303.37
101243.47
100822.77
100119.42
100112.27
101055.30
102251.73
103152.78
104275.29
105111.02
166865.29
177103.05
176812.34
178788.08
179360.68
176202.57
175804.27
175912.51
176585.97
177745.04
180039.72
180124.55
180111.12
179617.57
179593.31
179135.19
179539.79
180210.70
180232.33
179699.30
179407.53
179335.78
180079.34
180750.11
179211.47

Table B- 47. Data for Figures 6-11 and 6-12, experiment D response.
Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5

D8
3.23
3.28
2.69
3.26
7.81
14.42
17.15
15.39
13.54
11.31
7.32
6.39
7.11
10.19
10.85
11.91
9.87
9.66
9.34
7.76
7.14
4.93
3.96
3.84
3.06
19.83
22.40
21.87
21.00
16.85
13.94
13.71
16.01
18.36
18.78
16.82
15.86

D5
31.03
5.81
9.73
11.42
18.21
26.34
29.27
27.18
25.68
22.80
18.34
19.63
19.57
21.25
18.85
20.22
17.71
18.97
19.17
18.44
18.65
16.80
16.80
19.19
18.40
20.53
24.86
26.28
30.31
30.36
28.39
26.43
25.86
26.28
27.37
27.95
27.93

D3
33.33
33.64
32.28
30.53
32.30
36.89
37.40
34.27
31.92
30.61
28.71
30.33
30.23
30.64
28.86
30.42
28.24
29.53
27.33
23.37
23.23
22.89
24.72
27.97
23.37
39.93
46.47
46.01
44.70
40.12
37.26
35.66
35.87
36.96
38.22
37.51
37.46

D0
120.29
66.98
64.92
49.45
49.96
48.68
52.80
49.84
48.09
44.99
40.32
39.91
38.54
37.78
34.75
35.25
31.42
29.93
30.34
32.33
34.66
34.44
36.96
39.80
28.78
43.13
48.54
49.49
51.23
48.02
45.45
44.57
45.26
46.14
46.85
47.02
47.40
269

Table B-47 (cont’d).
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

16.26
17.23
17.27
19.20
22.48
25.08
25.06
23.85
21.78
20.46
19.25
17.91
18.08
29.10
3.83
7.70
7.02
10.31
13.31
13.49
11.55
10.85
12.46
12.88
12.39
11.65
11.94
11.16
10.71
10.36
10.63
11.44
13.12
13.10
13.45
12.38
10.21
8.49

28.23
28.74
28.75
28.65
28.40
28.53
28.80
29.48
26.65
24.64
23.90
24.77
23.60
42.49
17.39
26.80
20.48
17.12
14.05
14.32
13.10
13.17
12.98
12.65
12.51
12.21
12.46
12.83
13.81
15.01
16.32
17.86
17.37
15.45
15.44
15.49
13.18
13.57

37.59
38.34
37.51
37.85
39.36
40.02
39.28
39.32
38.52
35.33
34.09
35.11
31.87
29.86
29.39
29.94
26.36
23.47
21.89
22.61
20.72
18.36
17.27
17.85
18.37
18.49
18.98
19.60
20.01
20.19
20.65
21.11
20.89
19.20
18.68
18.01
16.08
15.72

47.84
47.75
47.17
46.29
45.88
45.43
44.72
44.14
43.14
41.66
41.11
42.47
42.43
44.10
40.35
39.83
40.87
43.54
42.57
41.92
40.15
39.85
40.25
41.42
40.86
40.18
40.05
40.21
40.18
40.32
41.51
42.14
41.56
38.84
38.11
37.80
36.04
34.25

270

Table B- 48. Data for Figure 6-11, experiment D average response.
Average Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

D8
17.38
9.83
10.76
10.43
11.66
13.89
14.78
14.32
14.25
14.18
12.34
11.54
11.67
13.12
13.09
13.94
14.24
15.12
15.28
14.91
14.01
12.95
11.86
10.65
9.88

D5
31.35
16.02
20.94
20.73
21.90
22.92
23.34
22.05
21.71
21.05
19.65
20.02
20.00
20.82
20.14
20.90
20.37
21.27
21.94
21.76
20.25
18.96
18.73
19.05
18.52

D3
34.37
36.50
36.07
33.87
31.96
32.01
31.89
30.29
29.08
28.70
28.02
28.72
28.77
29.32
28.66
29.43
29.26
30.07
29.24
27.86
26.98
25.63
25.61
26.39
23.65

D0
69.17
51.96
51.42
47.19
47.17
45.57
46.43
45.08
44.69
44.03
42.92
42.72
42.18
41.86
40.71
40.57
39.21
38.95
39.07
39.34
38.88
38.07
38.62
39.43
35.15

271

Table B- 49. Student’s T-Tests of responses (2 tails, α = 0.05).
Experiment A
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank

D0

D8

D5

D8

D3

D8
D5
D5
D3

D0
D3
D0
D0

Experiment B
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank
D8

D0
D5

D8

D3

D8

D0

D5

D3

D5
D3

D0
D0

P Value
1.184E15
4.789E17
5.119E17
4.609E18
5.841E08
3.520E10
5.520E12
0.047
0.002
0.241

P Value
6.987E07
1.118E13
2.023E23
4.766E21
0.053
6.691E08
5.612E07
9.785E05
4.830E04
0.778

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No

Significant
Difference
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
272

Experiment C
Sample Sample
1
2
Blank
D8
Blank
D5
Blank
D3
Blank
D0
D8
D5
D8
D3
D8
D0
D5
D3
D5
D0
D3
D0

P Value
5.356E-17
5.274E-24
1.184E-23
2.594E-14
4.952E-04
1.624E-07
1.895E-06
0.019
0.002
0.086

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No

Experiment D
Sample Sample
1
2
Blank
D8
Blank
D5
Blank
D3
Blank
D0
D8
D5
D8
D3
D8
D0
D5
D3
D5
D0
D3
D0

P Value
6.389E-30
4.912E-40
8.101E-45
5.567E-47
3.020E-12
6.822E-29
5.474E-41
3.800E-11
9.559E-30
6.303E-15

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

273

Experiment E
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank

D0

D8

D5

D8

D3

D8

D0

D5

D3

D5

D0

D3

D0

Experiment F
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank
D8

D0
D5

D8

D3

D8
D5
D5
D3

D0
D3
D0
D0

P Value
5.694E08
7.213E34
7.093E36
1.306E35
5.093E23
1.842E36
1.231E46
4.055E40
5.939E39
1.606E30

Significant Difference

P Value
3.276E04
1.023E04
1.054E07
5.397E07
0.071
7.520E05
4.623E05
0.020
0.003
0.274

Significant Difference

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No

274

Experiment G
Sample Sample
1
2
Blank

D8

Blank

D5

Blank

D3

Blank

D0

D8

D5

D8

D3

D8

D0

D5

D3

D5

D0

D3

D0

Experiment H
Sample Sample
1
2
Blank
D8
Blank
D5
Blank
D3
Blank
D0
D8
D5
D8
D3
D8
D0
D5
D3
D5
D0
D3
D0

P Value
2.02E21
5.50E18
4.24E32
4.00E36
3.29E09
8.38E29
6.01E36
1.80E09
3.42E22
1.29E10

P Value
5.237E-19
2.111E-18
1.642E-19
1.085E-20
4.976E-13
2.299E-16
2.371E-18
4.851E-05
9.095E-07
0.363

Significant Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No

275

Table B- 50. Data for Figure 6-12, experiment E resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871

B
5780.35
5785.72
5790.03
5828.35
5839.29
5896.23
5919.79
5935.72
5935.60
5966.33
5969.26
5978.82
5975.50
5989.43
6040.60
6070.20
6044.51
6044.90
6053.92
6074.94
6078.63
6111.80
6106.87
6102.21

D8
6032.01
6073.49
6030.58
5993.53
5973.33
5962.52
5960.24
5959.35
5987.13
6017.66
6035.52
6056.18
6055.63
6056.71
6060.87
6128.93
6168.89
6187.16
6179.92
6213.38
6241.08
6272.12
6294.26
6259.78

D5
6330.76
6344.57
6367.62
6394.30
6429.32
6468.16
6514.05
6549.71
6580.18
6585.64
6590.41
6586.88
6608.61
6635.58
6660.32
6659.85
6666.15
6676.52
6696.76
6706.27
6732.51
6756.59
6768.19
6762.47

D3
6795.79
6884.00
6919.39
6948.41
6978.89
7026.70
7056.27
7068.56
7099.21
7125.41
7133.94
7143.22
7169.38
7164.97
7187.78
7220.59
7264.61
7260.09
7256.71
7274.62
7317.58
7349.27
7346.19
7359.71

276

D0
7326.77
7353.91
7390.40
7425.68
7475.04
7521.62
7541.53
7540.87
7550.48
7556.34
7558.63
7590.34
7619.05
7609.73
7582.63
7582.94
7583.45
7584.48
7598.27
7646.62
7678.18
7690.09
7684.92
7648.18

Table B- 51. Data for Figure 6-12, experiment F resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
180
210
240
270
300
330
360
390
421
451
481
511
541

B
10011.44
10046.46
10072.67
10134.68
10181.85
10265.75
10266.11
10278.15
10268.71
10276.64
10278.25
10286.89
10306.13
10338.28
10352.39
10359.24
10411.55
10466.20
10487.10
10468.46
10450.78
10438.48
10432.65
10426.17
15924.47
15733.21
15848.94
15871.30
15938.16
15886.89
15952.24
16014.96
16055.60
16019.45
15976.76
15979.78
15982.42

D8
10574.42
10641.53
10748.77
10813.44
10882.49
10892.99
10944.51
10941.90
10966.72
11046.28
11082.22
11133.33
11156.27
11223.30
11212.87
11272.50
11296.38
11388.55
11443.89
11539.59
11612.18
11733.99
11765.64
11737.09
16257.23
16117.10
16110.19
16071.36
16082.25
16022.15
15962.76
15933.26
16042.97
16098.15
16128.36
16111.30
16105.70

D5
12062.73
12232.95
12226.97
12326.44
12441.35
12490.13
12535.26
12614.20
12718.25
12745.00
12796.49
12789.59
12805.09
12874.51
12974.13
13050.33
13142.70
13229.14
13277.69
13279.58
13338.67
13396.36
13471.25
13531.80
15311.95
15091.90
15021.96
14862.55
14886.91
14965.74
15102.40
15236.85
15293.36
15317.29
15291.94
15256.94
15115.28

D3
13818.97
13904.75
13908.12
13947.77
14039.52
14141.92
14164.84
14200.42
14182.46
14180.07
14167.53
14250.81
14352.35
14427.04
14461.17
14506.27
14558.31
14594.96
14679.28
14727.00
14800.64
14828.23
14932.46
15034.77
15110.22
15392.23
15531.05
15536.35
15730.51
15882.69
15925.01
15948.46
15944.33
16052.37
16012.90
15937.98
15769.40

277

D0
15603.23
15516.76
15599.23
15705.01
15608.88
15577.54
15568.87
15633.72
15715.33
15834.32
15884.59
15887.70
15928.33
15891.03
15900.28
15995.24
16165.35
16211.59
16233.67
16227.93
16256.48
16306.09
16399.59
16423.73
15476.46
15371.10
15205.86
15123.45
15043.14
14941.54
14963.74
15000.00
15007.73
14966.36
14958.01
15058.45
15047.28

Table B-51 (cont’d).
571
601
631
661
691
721
751
781
811
841
871

16011.71
16050.58
16108.71
16175.62
16219.85
16243.56
16329.96
16441.38
16422.38
9893.75
16447.37

16061.57
16023.72
15952.54
15889.70
15876.97
15878.32
15880.25
15838.98
15813.41
15762.39
15717.09

15047.29
14992.08
14950.48
14926.87
14908.84
14848.34
14756.88
14752.68
14856.55
14946.32
14953.27

15597.45
15547.78
15647.47
15665.69
15712.85
15544.89
15419.25
15320.81
15463.89
15481.59
15533.98

278

15073.56
15074.67
14983.04
14799.00
14748.56
14927.20
15177.71
15458.77
15640.97
15695.86
15710.92

Table B- 52. Data for Figure 6-12, experiment G resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901
180
210
240
270
300
330
360
390
421
451
481
511

B
26987.96
27068.83
27351.76
27897.89
28000.98
28049.36
28210.88
28171.00
28195.81
28322.02
28446.90
28188.87
27892.34
27918.19
28030.13
28045.99
27898.86
27709.60
27854.18
28387.78
28953.43
29037.81
29108.15
29143.90
28149.19
36377.82
35804.13
36189.87
36191.60
35952.81
35708.22
36036.35
35783.53
35946.55
36432.00
37193.97
36724.98

D8
28498.73
28928.61
29179.73
29136.02
29016.14
29375.05
29466.24
29707.90
29731.25
29918.05
29811.02
30091.35
30472.87
31046.51
30990.59
30652.15
30264.46
30019.46
29600.27
29383.88
29735.36
30156.82
30490.80
30698.39
30429.82
38633.99
39695.20
40394.70
40525.96
40160.06
40057.23
40122.94
40065.84
39859.06
39308.18
39292.45
38881.88

D5
29595.96
29535.87
29544.59
29545.52
29832.62
30058.72
30013.28
29874.31
29875.02
29982.76
30064.88
30161.51
30147.89
30240.77
30141.89
30080.91
30220.94
30533.61
30614.82
30639.17
30625.86
30554.26
30640.62
30983.73
31043.85
41925.10
42195.98
42081.17
42251.57
42852.58
43707.13
44287.80
44392.32
44329.03
44764.20
45129.98
45305.98

D3
31624.11
31474.81
31722.65
32173.54
32611.32
32674.78
32594.93
32590.64
32825.44
33035.42
33068.98
33005.24
32955.75
33073.20
33233.65
33512.58
34061.66
34511.65
34609.24
34572.36
34694.25
35005.33
34837.14
34379.03
35211.27
45441.45
45964.42
45974.73
46057.61
46126.78
46107.43
45929.25
45958.12
46030.56
46395.45
46789.62
47010.64

279

D0
35193.57
35445.28
36021.19
35764.46
35572.15
35459.51
35506.85
35496.26
35465.92
35451.41
35448.87
35605.28
35592.61
35656.06
35504.61
35669.24
35691.98
35863.40
35723.85
35617.51
35769.04
36116.44
36406.19
36613.27
37383.18
50197.20
49991.05
50350.43
50398.18
50379.64
50375.28
50101.99
49647.55
49555.78
50011.61
50542.66
50811.16

Table B-52 (cont’d).
541
571
601
631
661
691
721
751
781
811
841
871
901

36325.20
36593.23
36246.65
36220.23
36203.55
36819.39
36530.64
36417.85
36366.89
36645.98
36987.56
37243.95
36934.44

38021.25
37457.20
37942.43
38354.06
37972.02
37810.24
38073.22
38231.53
38321.90
38313.65
38706.79
39005.36
38186.16

45264.97
45276.69
45099.45
45207.75
45362.09
45307.42
45393.51
45111.09
45020.02
45181.69
45154.72
44469.15
45325.78

46748.46
46623.92
46955.46
47365.89
47639.65
47647.41
47795.57
47521.24
47599.96
48191.10
49267.15
49597.02
49291.44

280

51330.23
51894.14
52034.21
52086.24
52193.04
52334.50
51991.22
51706.24
50951.71
50210.42
48585.81
47365.30
49720.32

Table B- 53. Data for Figure 6-12, experiment H resistance.
Resistance ( )
Time
(s)
180
210
240
270
300
330
360
390
421
451
481
511
541
571
601
631
661
691
721
751
781
811
841
871
901

B
55456.53
55688.14
55843.64
56556.52
57340.03
57760.54
58118.41
57764.12
58158.25
58045.52
59863.59
60235.78
61091.36
60971.62
62592.21
63091.47
63919.64
65095.08
66835.34
68209.49
68945.14
70170.16
71580.13
73193.05
74074.07

D8
72774.53
73413.73
72768.85
72799.12
73356.03
75708.41
76523.64
76946.33
78600.90
80114.44
80728.86
79776.63
79974.86
81350.42
82046.46
83729.56
87697.31
94931.35
100639.77
100160.96
100589.15
102594.18
106890.62
106100.80
105263.16

D5
103780.58
110182.01
107226.28
106082.70
106601.69
109443.39
111084.67
110348.31
109383.53
110431.86
110970.62
108798.28
107566.53
109466.94
112511.25
112218.16
109661.58
108017.19
108632.81
109572.30
109145.62
108814.76
107878.59
106951.87
109036.39

281

D3
116569.52
125000.00
125184.43
124533.00
123147.28
122205.08
121883.26
121460.81
121016.18
121501.40
121548.86
121888.05
121488.23
121873.73
122948.30
123097.50
122188.18
121271.42
122391.65
121872.66
121472.03
121076.90
122292.09
123039.06
121784.14

D0
120249.08
125000.00
125058.62
124816.11
124521.22
124365.09
124285.79
124021.66
124243.82
124713.28
124548.20
123860.67
123594.24
124175.63
125263.55
126286.54
127593.33
127026.16
126380.58
125420.50
125825.73
125529.58
125791.23
125845.52
126043.80

Table B- 54. Data for Figure 6-12, cumulative response experiments A-H.
Response (%)

Exp E

Exp F

Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0

D8
4.35
4.97
4.15
2.83
2.30
1.12
0.68
0.40
0.87
0.86
1.11
1.29
1.34
1.12
0.34
0.97
2.06
2.35
2.08
2.28
2.67
2.62
3.07
2.58
5.62
5.92
6.71
6.70
6.88
6.11
6.61
6.46
6.80
7.49
7.82
8.23
8.25

D5
9.52
9.66
9.98
9.71
10.10
9.70
10.04
10.34
10.86
10.38
10.41
10.17
10.60
10.79
10.26
9.71
10.28
10.45
10.62
10.39
10.76
10.55
10.83
10.82
20.49
21.76
21.39
21.63
22.19
21.67
22.10
22.73
23.85
24.02
24.50
24.33
24.25

D3
17.57
18.98
19.51
19.22
19.52
19.17
19.20
19.09
19.60
19.43
19.51
19.48
19.98
19.63
18.99
18.95
20.19
20.10
19.87
19.75
20.38
20.25
20.29
20.61
38.03
38.40
38.08
37.62
37.89
37.76
37.98
38.16
38.11
37.98
37.84
38.53
39.26
282

D0
26.75
27.10
27.64
27.41
28.01
27.57
27.40
27.04
27.21
26.65
26.63
26.95
27.50
27.05
25.53
24.92
25.46
25.47
25.51
25.87
26.31
25.82
25.84
25.33
55.85
54.45
54.87
54.96
53.30
51.74
51.65
52.11
53.04
54.08
54.55
54.45
54.55

Table B-54 (cont’d).

Exp G

9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
3.0
3.5
4.0
4.5

8.56
8.31
8.82
8.50
8.81
9.12
10.23
11.11
12.41
12.78
12.57
2.09
2.44
1.65
1.26
0.90
0.85
0.07
-0.51
-0.08
0.49
0.95
0.82
0.77
0.31
-0.17
-0.97
-1.77
-2.11
-2.25
-2.75
-3.66
-3.71
59.32
-4.44
5.60
6.87
6.68
4.44

24.53
25.33
25.98
26.23
26.40
26.61
26.85
27.63
28.34
29.13
29.79
-3.85
-4.08
-5.22
-6.36
-6.60
-5.80
-5.33
-4.86
-4.75
-4.38
-4.29
-4.52
-5.43
-6.02
-6.59
-7.19
-7.72
-8.08
-8.59
-9.63
-10.27
-9.53
51.07
-9.08
9.66
9.11
8.02
5.91

39.55
39.69
40.03
39.83
39.45
39.97
40.68
41.62
42.05
43.13
44.20
-5.11
-2.17
-2.01
-2.11
-1.30
-0.03
-0.17
-0.42
-0.69
0.21
0.23
-0.26
-1.33
-2.59
-3.13
-2.86
-3.15
-3.13
-4.30
-5.58
-6.82
-5.84
56.48
-5.55
17.18
16.28
15.98
15.33
283

53.71
53.59
54.41
55.26
54.89
54.80
55.02
55.55
56.21
57.19
57.52
-2.81
-2.30
-4.06
-4.71
-5.62
-5.95
-6.20
-6.34
-6.53
-6.57
-6.38
-5.77
-5.85
-5.86
-6.08
-6.99
-8.51
-9.07
-8.10
-7.06
-5.98
-4.76
58.64
-4.48
30.40
30.95
31.70
28.20

Table B-54 (cont’d).
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5

3.63
4.73
4.45
5.46
5.45
5.64
4.80
6.75
9.25
11.21
10.56
9.29
8.48
8.34
6.27
3.51
2.70
3.85
4.75
5.33
8.10
6.20
10.87
11.62
11.98
11.70
12.18
11.34
11.97
10.88
7.89
5.64
5.87
4.67
2.36
4.68
5.89
4.88
2.69

6.54
7.16
6.39
6.05
5.96
5.86
5.69
7.00
8.09
8.32
7.53
7.26
8.32
10.19
9.91
7.93
5.78
5.22
5.26
6.31
10.28
15.25
17.85
16.28
16.74
19.19
22.40
22.90
24.06
23.32
22.87
21.34
23.37
24.61
23.73
24.42
24.81
25.30
23.05

16.46
16.49
15.54
15.69
16.42
16.64
16.25
17.09
18.15
18.46
18.56
19.49
22.09
24.55
24.25
21.79
19.83
20.55
19.68
17.96
25.09
24.92
28.38
27.04
27.26
28.30
29.12
27.45
28.43
28.05
27.35
25.80
28.01
28.69
27.41
29.54
30.77
31.59
29.41
284

27.04
26.42
25.86
26.00
25.78
25.17
24.61
26.31
27.61
27.72
26.67
27.18
27.93
29.43
28.25
25.47
23.54
24.38
25.07
25.63
32.80
37.99
39.62
39.13
39.25
40.13
41.07
39.03
38.74
37.86
37.27
35.89
38.36
41.31
41.81
43.56
43.80
44.17
42.14

Table B-54 (cont’d).

Exp A

12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0

4.22
4.98
5.38
4.55
4.65
4.73
3.39
9.29
10.06
8.91
8.41
9.00
9.22
9.24
9.45
9.62
9.78
10.59
12.09
12.65
12.50
11.75
11.21
11.19
11.63
12.15
12.85
13.33
13.04
12.30
11.80
11.74
28.39
27.24
29.78
31.27
34.05
36.14
36.95

24.26
23.87
23.79
23.29
22.08
19.40
22.72
11.53
11.97
11.97
12.07
12.96
12.91
13.11
13.14
13.46
13.15
13.36
15.10
17.27
17.60
16.58
16.15
16.13
16.30
16.51
16.66
16.95
16.51
16.51
16.96
16.11
102.01
100.50
103.64
105.79
106.91
105.59
105.46

30.84
30.49
30.89
31.50
33.20
33.17
33.46
17.63
19.24
18.77
19.15
18.73
19.04
18.83
19.75
20.56
21.54
22.28
23.48
24.58
24.68
24.68
24.58
24.65
24.65
24.63
24.64
24.73
24.77
24.71
24.71
24.80
146.47
148.86
149.07
147.08
146.12
144.84
142.52
285

42.32
41.98
40.10
37.01
31.36
27.18
34.62
23.28
24.43
23.62
23.42
22.63
21.62
21.41
22.00
22.86
22.82
23.35
24.28
25.61
25.45
24.68
23.76
23.61
23.95
24.58
25.07
25.34
25.17
25.28
26.47
29.25
158.41
161.66
162.28
159.01
159.27
157.27
158.54

Table B-54 (cont’d).
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0

36.71
37.58
40.47
43.59
43.87
42.96
44.33
47.10
49.85
47.52
49.99
52.09
54.52
51.99
52.35
52.94
53.76
55.11
8.07
8.02
8.25
7.94
6.86
7.15
8.10
9.55
10.63
10.78
9.00
8.01
6.25
5.02
4.11
4.70
4.68
4.38
3.88
3.84
4.35

103.43
102.26
102.12
104.58
105.67
104.57
104.45
105.08
105.73
104.11
103.69
103.45
100.96
98.22
96.23
99.17
102.58
96.44
17.82
18.21
18.93
20.61
22.12
23.24
24.24
26.43
28.35
29.60
30.16
31.17
31.25
31.28
31.64
33.03
33.38
33.75
34.08
34.86
35.79

140.02
139.10
138.55
137.50
134.93
131.66
131.52
132.24
132.81
130.46
129.61
128.37
125.80
122.21
119.40
119.12
118.91
117.13
22.47
22.22
22.55
23.33
24.23
25.28
26.42
28.67
31.35
33.91
34.67
35.46
36.02
37.03
37.91
40.25
41.48
42.09
42.41
43.39
44.19
286

158.96
158.20
157.31
156.82
153.85
150.83
147.75
148.59
150.70
149.96
144.77
141.12
140.88
142.19
142.54
142.03
140.93
139.19
33.11
33.50
33.68
34.74
36.07
37.48
38.68
41.14
43.66
45.89
46.95
48.45
48.86
49.35
50.18
52.10
53.29
54.09
54.59
55.46
56.82

Table B-54 (cont’d).

Exp H

Exp B

13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5

4.85
5.16
5.23
5.15
31.23
31.83
30.31
28.72
27.93
31.07
31.67
33.21
35.15
38.02
34.85
32.44
30.91
33.42
31.08
32.71
37.20
45.83
50.58
46.84
45.90
46.21
49.33
44.96
42.11
11.71
5.36
2.51
5.00
6.94
5.03
3.46
1.08
-0.76
-2.81

36.90
37.34
36.98
36.85
87.14
97.86
92.01
87.57
85.91
89.48
91.14
91.03
88.08
90.25
85.37
80.62
76.07
79.54
79.75
77.87
71.56
65.94
62.54
60.64
58.31
55.07
50.71
46.12
47.20
27.32
19.49
15.16
16.76
18.46
15.72
10.75
5.77
2.92
4.79

44.98
46.12
47.09
47.20
110.20
124.46
124.17
120.19
114.77
111.57
109.72
110.27
108.08
109.32
103.04
102.35
98.86
99.89
96.43
95.11
91.16
86.30
83.12
78.67
76.19
72.55
70.85
68.10
64.41
27.90
21.06
15.41
16.76
22.83
21.57
18.71
15.65
13.69
11.35
287

57.90
58.79
58.85
56.46
116.83
124.46
123.94
120.69
117.16
115.31
113.85
114.70
113.63
114.85
108.05
105.63
102.31
103.66
100.13
100.16
99.62
95.14
89.09
83.88
82.50
78.89
75.73
71.94
70.16
24.52
17.30
14.58
17.24
19.72
17.72
16.74
13.63
11.44
9.15

Table B-54 (cont’d).
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5

-3.78
-5.43
-3.16
-2.57
0.12
3.58
12.32
14.93
13.79
8.34
7.65
7.99
10.37
9.97
5.02
-14.75
-14.09
3.36
4.63
5.44
4.56
6.00
1.54
1.33
4.56
2.50
-1.63
-6.09
-5.38
3.20
10.56
10.90
5.58
-2.62
-10.22
-12.14
-12.28
-14.08
-15.43

5.90
5.56
4.54
5.32
8.66
13.42
19.98
21.40
20.30
16.94
17.29
17.39
17.09
14.68
8.68
-7.41
-8.30
-4.30
1.88
5.55
8.66
7.64
4.94
2.00
3.05
3.44
4.29
3.65
3.96
7.14
11.37
12.09
10.53
7.74
3.45
2.07
0.97
-0.27
-1.75

9.39
10.07
10.95
13.96
15.33
19.54
26.21
29.19
27.48
22.60
22.12
21.06
20.67
17.24
12.30
9.76
10.36
10.60
10.62
11.85
15.22
16.80
13.24
10.85
12.96
16.87
19.38
18.93
17.78
20.05
23.20
22.77
22.19
20.54
18.51
14.95
15.34
10.97
5.63
288

9.61
8.25
9.21
10.47
14.30
17.95
23.37
24.94
25.54
22.09
22.03
21.24
22.15
18.57
10.46
15.00
13.13
17.23
23.21
23.11
20.33
17.75
15.35
11.21
7.58
5.91
6.83
8.05
9.84
12.67
16.90
16.52
13.26
13.09
11.75
14.61
13.62
12.68
7.23

Table B-54 (cont’d).

Exp C

15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0

-16.00
25.73
8.54
1.03
4.77
9.97
14.89
20.57
28.52
36.18
38.03
40.47
40.30
38.07
40.43
47.23
48.70
42.61
41.16
40.34
32.75
25.94
25.07
27.34
30.99
52.94
25.12
-0.08
1.93
6.56
11.76
11.99
7.04
5.36
8.00
9.77
11.22
11.49
12.74

-0.32
18.75
7.45
-0.96
2.45
9.46
18.26
25.27
31.70
35.47
38.55
40.12
39.40
37.79
45.22
50.87
48.41
42.41
41.19
41.15
34.59
31.47
31.43
33.48
35.30
55.98
41.08
39.53
36.07
31.37
26.77
22.98
16.87
12.70
12.30
15.27
21.03
25.03
27.19

13.80
76.92
30.01
24.47
27.66
32.52
37.09
43.12
50.26
52.97
51.94
51.38
53.23
50.73
54.01
55.36
52.39
44.28
40.48
39.22
36.29
33.87
36.69
40.65
45.47
65.47
123.56
80.89
90.28
76.32
51.20
33.01
26.08
28.49
34.42
41.10
44.07
44.55
42.90
289

8.14
45.94
35.64
30.01
33.71
37.97
43.33
50.65
60.88
66.54
64.69
56.53
52.19
51.33
55.87
54.59
49.91
43.32
40.81
39.53
33.96
29.49
31.27
37.06
45.42
68.72
40.24
26.04
27.03
27.23
24.80
22.99
14.90
13.12
13.89
19.30
22.75
25.04
23.40

Table B-54 (cont’d).
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5

13.16
14.02
15.59
17.09
17.63
17.96
19.89
21.99
25.14
28.40
32.59
34.76
76.48
43.95
52.51
50.57
46.14
44.19
44.35
42.97
36.72
34.75
36.49
40.73
40.77
36.24
33.42
32.67
30.51
26.87
28.50
28.96
26.82
23.42
18.25
11.78
15.64
-0.08
0.40

25.61
24.83
20.66
17.10
16.13
20.92
26.89
29.66
32.17
30.24
29.70
48.99
86.04
49.82
58.03
60.88
61.50
60.91
57.12
51.96
44.51
43.65
45.17
49.62
50.80
47.34
47.97
46.11
45.51
39.55
38.48
32.76
31.76
32.11
31.98
27.33
21.60
22.58
-1.02

39.92
38.40
39.81
40.55
41.92
40.36
41.50
40.38
38.15
37.10
44.67
52.12
83.74
46.84
51.11
51.70
52.97
53.25
54.38
55.92
53.05
49.73
49.10
52.25
51.10
47.31
43.92
42.15
40.94
37.40
33.76
32.52
35.19
44.11
45.85
38.72
40.04
3.23
2.94
290

21.72
20.74
21.43
21.98
21.01
18.90
18.98
22.18
27.30
31.74
35.22
42.77
226.51
143.43
137.98
118.78
108.46
102.88
109.81
106.61
92.85
87.04
92.42
97.54
94.13
88.10
92.77
92.60
91.08
82.38
80.90
75.47
71.22
65.26
66.28
67.38
57.77
5.10
5.46

Table B-54 (cont’d).

Exp D

4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5

0.09
0.77
0.80
1.92
2.36
3.18
4.30
6.13
7.75
7.84
8.46
8.91
10.02
11.46
13.48
15.33
15.97
15.15
13.23
11.88
8.03
4.43
11.90
3.23
3.28
2.69
3.26
7.81
14.42
17.15
15.39
13.54
11.31
7.32
6.39
7.11
10.19
10.85
11.91

3.14
3.36
4.60
4.52
5.30
8.28
12.29
15.10
18.20
20.61
21.86
20.69
19.62
18.20
19.35
21.29
24.22
24.60
23.16
18.69
13.14
9.10
12.49
31.03
5.81
9.73
11.42
18.21
26.34
29.27
27.18
25.68
22.80
18.34
19.63
19.57
21.25
18.85
20.22

4.03
5.80
7.65
9.42
12.25
13.47
13.75
14.65
15.49
15.66
15.44
14.98
15.03
16.81
18.52
19.84
20.93
20.45
19.74
19.19
15.36
10.01
17.57
33.33
33.64
32.28
30.53
32.30
36.89
37.40
34.27
31.92
30.61
28.71
30.33
30.23
30.64
28.86
30.42
291

6.75
7.43
8.14
8.95
9.73
10.57
12.31
14.64
18.05
21.31
23.34
21.83
20.17
22.49
22.92
24.01
26.08
26.69
22.91
18.15
14.24
11.44
14.94
120.29
66.98
64.92
49.45
49.96
48.68
52.80
49.84
48.09
44.99
40.32
39.91
38.54
37.78
34.75
35.25

Table B-54 (cont’d).
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
3.0
3.5
4.0
4.5
5.0

9.87
9.66
9.34
7.76
7.14
4.93
3.96
3.84
3.06
19.83
22.40
21.87
21.00
16.85
13.94
13.71
16.01
18.36
18.78
16.82
15.86
16.26
17.23
17.27
19.20
22.48
25.08
25.06
23.85
21.78
20.46
19.25
17.91
18.08
29.10
3.83
7.70
7.02
10.31

17.71
18.97
19.17
18.44
18.65
16.80
16.80
19.19
18.40
20.53
24.86
26.28
30.31
30.36
28.39
26.43
25.86
26.28
27.37
27.95
27.93
28.23
28.74
28.75
28.65
28.40
28.53
28.80
29.48
26.65
24.64
23.90
24.77
23.60
42.49
17.39
26.80
20.48
17.12

28.24
29.53
27.33
23.37
23.23
22.89
24.72
27.97
23.37
39.93
46.47
46.01
44.70
40.12
37.26
35.66
35.87
36.96
38.22
37.51
37.46
37.59
38.34
37.51
37.85
39.36
40.02
39.28
39.32
38.52
35.33
34.09
35.11
31.87
29.86
29.39
29.94
26.36
23.47
292

31.42
29.93
30.34
32.33
34.66
34.44
36.96
39.80
28.78
43.13
48.54
49.49
51.23
48.02
45.45
44.57
45.26
46.14
46.85
47.02
47.40
47.84
47.75
47.17
46.29
45.88
45.43
44.72
44.14
43.14
41.66
41.11
42.47
42.43
44.10
40.35
39.83
40.87
43.54

Table B-54 (cont’d).
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

13.31
13.49
11.55
10.85
12.46
12.88
12.39
11.65
11.94
11.16
10.71
10.36
10.63
11.44
13.12
13.10
13.45
12.38
10.21
8.49

14.05
14.32
13.10
13.17
12.98
12.65
12.51
12.21
12.46
12.83
13.81
15.01
16.32
17.86
17.37
15.45
15.44
15.49
13.18
13.57

21.89
22.61
20.72
18.36
17.27
17.85
18.37
18.49
18.98
19.60
20.01
20.19
20.65
21.11
20.89
19.20
18.68
18.01
16.08
15.72

293

42.57
41.92
40.15
39.85
40.25
41.42
40.86
40.18
40.05
40.21
40.18
40.32
41.51
42.14
41.56
38.84
38.11
37.80
36.04
34.25

Table B- 55. Data for Figure 6-12, cumulative average response.
Average Response (%)
Time
(min)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0

D8
15.40
10.10
11.21
11.51
12.18
12.94
13.18
13.24
13.63
14.13
13.89
13.74
13.49
13.83
14.73
15.94
16.24
16.60
16.55
15.33
14.40
14.24
17.68
13.49
17.30

D5
30.67
24.33
24.83
25.14
26.19
27.01
26.83
26.33
25.89
26.52
26.88
27.64
27.62
28.04
28.53
28.57
28.06
27.53
27.67
26.50
25.73
25.12
28.00
23.97
28.84

D3
45.42
39.79
39.85
38.75
37.76
37.16
36.92
37.08
37.03
37.38
37.08
37.80
37.35
37.31
37.16
37.85
37.74
37.46
36.62
35.28
34.47
34.54
37.83
33.89
38.96

D0
58.04
49.49
48.92
47.34
46.76
45.86
46.06
46.10
45.45
45.11
44.69
45.10
44.93
44.67
44.68
45.17
44.87
43.56
42.97
41.86
41.42
40.79
44.44
40.72
44.72

294

Table B- 56. Student’s T-Test cumulative responses of experiments A – H (2 tails, α = 0.05)

Sample 1
Blank
Blank
Blank
Blank
Blank
Blank
Blank
Blank
10^0 CFU/mL
10^0 CFU/mL
10^0 CFU/mL
10^0 CFU/mL
10^0 CFU/mL
10^0 CFU/mL
10^0 CFU/mL
10^1 CFU/mL
10^1 CFU/mL
10^1 CFU/mL
10^1 CFU/mL
10^1 CFU/mL
10^1 CFU/mL

Sample 2
10^0
CFU/mL
10^1
CFU/mL
10^3
CFU/mL
10^4
CFU/mL
10^5
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^1
CFU/mL
10^3
CFU/mL
10^4
CFU/mL
10^5
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^3
CFU/mL
10^4
CFU/mL
10^5
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL

P Value
1.544E45
4.759E20
5.118E57
7.782E21
4.086E72
7.392E23
3.775E67
5.329E26
0.588
8.696E11
6.794E10
5.894E29
4.534E14
3.060E34
5.694E18
1.645E06
9.530E09
1.629E19
3.875E13
2.097E26
3.841E17
295

Significant
Difference
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Table B-56 (cont’d).

10^3 CFU/mL
10^3 CFU/mL
10^3 CFU/mL
10^3 CFU/mL
10^3 CFU/mL
10^4 CFU/mL
10^4 CFU/mL
10^4 CFU/mL
10^4 CFU/mL
10^5 CFU/mL
10^5 CFU/mL
10^5 CFU/mL
10^6 CFU/mL
10^6 CFU/mL
10^8 CFU/mL

10^4
CFU/mL
10^5
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^5
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^6
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^8
CFU/mL
10^9
CFU/mL
10^9
CFU/mL

0.001
1.470E07
2.395E08
3.954E14
2.443E12

Yes

0.592

No

0.013

Yes

0.152
3.404E05

No
Yes

0.001

Yes

0.002
2.882E07

Yes

0.090

No

0.092
2.904E04

No

296

Yes
Yes
Yes
Yes

Yes

Yes

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