IMPACT OF ALKALINE HYDROGEN PEROXIDE PRETREATMENT ON CELL
WALL PROPERTIES THAT CONTRIBUTE TO IMPROVED ENZYMATIC
DIGESTIBILITY OF STRUCTURAL CARBOHYDRATES TO BE UTILIZED FOR
BIOFUEL PRODUCTION
By
Daniel Williams

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Chemical Engineering—Doctor of Philosophy
2014

ABSTRACT
IMPACT OF ALKALINE HYDROGEN PEROXIDE PRETREATMENT ON CELL
WALL PROPERTIES THAT CONTRIBUTE TO IMPROVED ENZYMATIC
DIGESTIBILITY OF STRUCTURAL CARBOHYDRATES TO BE UTILIZED FOR
BIOFUEL PRODUCTION
By
Daniel Williams
Lignocellulosic plant material is an attractive option as a source of sugars that can be
converted to fuels such as ethanol due to it being an abundant and renewable resource. One of
the more compelling process schemes to do this is the biochemical conversion platform, where
enzymes are used to hydrolyze sugar polymer bonds and release monomeric sugars that can be
used by fermenting organisms to produce the desired fuel. However, due to the recalcitrant
nature of lignocelluloses, a pretreatment step is usually required before hydrolysis to improve
cell wall polysaccharide accessibility to enzymes in order to facilitate enzyme catalysis and
ultimately cell wall deconstruction to soluble sugar monomers. Within this pretreatment step it
is necessary to increase polysaccharide accessibility by removing or redistributing lignin and
hemicelluloses and increasing cell wall porosity. This work investigates pretreatment, primarily
alkaline and alkaline hydrogen peroxide (AHP) pretreatment, in two ways: 1) as a unit operation
integrated with enzymatic hydrolysis and fermentation for a complete conversion process and 2)
as a tool for investigating cell wall properties that are important for improved deconstruction,
more specifically, enzymatic digestibility. Two studies in each category are presented in this
work.
In the first, corn stover and switchgrass were AHP pretreated over a range of
pretreatment conditions to understand the space of changes that take place during the process;
specifically, the impact of H2O2 loading, feedstock, pretreatment time, solids loading and scale

were determined on compositional changes of solid biomass, inhibitor release and pretreatment
effectiveness measured by enzymatic digestibility.
In the next study, soluble sugars from a sweet sorghum were simultaneously extracted
while the remaining lignocellulose in bagasse was alkali pretreated in a novel countercurrent
diffusion extraction/pretreatment technique. The carbohydrates in the bagasse were then
hydrolyzed with enzymes and the hydrolyzate was combined with the extraction juice and
fermented. Near 100% soluble sugar extraction was achieved and a glucose yield of 70% was
obtained on the pretreated bagasse. An ethanol concentration of 21 g/L was obtained
corresponding to 85% ethanol yield indicating that this combined technique has potential.
In the last two studies, absorbed water within the solid matrix of corn stover and
switchgrass, AHP and liquid hot water (LHW) pretreated, was quantified by water retention
value (WRV) and settling volume and found to be linearly correlated with glucose yield a fter
hydrolysis. Results indicate that AHP and LHW pretreatment can increase water binding to
biomass surfaces and increase swelling, which is indicative of increased surface accessible not
only to water molecules, but also to enzymes.
The follow up study expanded the range of AHP and LHW pretreatment conditions and
included ammonia fiber expansion (AFEX) pretreatment of corn stover and switchgrass and
found that linear regression of WRV with glucose yield does not fit for all pretreatment types and
conditions. WRV for AFEX pretreated material does linearly correlate with glucose yield,
however, not with the same slope as AHP and LHW pretreated material. A multiple linear
regression model was developed to include composition features of the pretreated biomass with
WRV and yielded much better prediction results across all pretreatments and conditions.

ACKNOWLEDGEMENTS

I would like to thank my advisor Dr. David Hodge for all his help and patience in guiding
me through this challenging endeavor, and for always being encouraging when it was most
needed. I’m greatful to my committee for providing expert perspective and constructive
criticism for my work. I want to thank my labmates Ryan, Glen, Tongjun, and Muyang for all
the support and enjoyment you’ve brought to the lab over the years, it has been a great pleasure
working with you all. To my friends and collegues Bhushan, Chloe, Dena, and Ankush your
support and encouragement from the start have been greatly appreciated and I don’t think I could
have done this without it. To the many others who I have worked with over the course of my
time here at Michigan State University, it has been true pleasure.
I would also like to thank Rebecca Garlock-Ong for providing the AFEX pretreated
material used in this work, and Mike Rich for training me to use DSC and FTIR. And a special
thanks to Kristine Van Winkle for everything she did to help maintain equipment in the lab and
for all her invaluable help on the many lab related challenges that were encountered over the
years.

iv

TABLE OF CONTENTS

LIST OF TABLES............................................................................................................................ viii
LIST OF FIGURES .............................................................................................................................ix
KEY TO ABBREVIATIONS ............................................................................................................xii
Introduction ........................................................................................................................................... 1
Motivation – Biofuels and Sustainable Energy ...................................................................... 1
Challenge - Cellulosic Ethanol ................................................................................................ 6
Background Literature Review – AHP Pretreatment, Hydrolysis and Fermentation .......... 9
AHP Pretreatment - pH Control ................................................................................... 11
Temperature and Pretreatment Time ............................................................................ 11
Hydrogen Peroxide and Solids Concentrations ........................................................... 12
Biomass Structural and Compositional Changes ......................................................... 15
Enzymatic Hydrolysis.................................................................................................... 17
Fermentation Inhibitors ................................................................................................. 19
Cell Wall and Water Interactions .................................................................................. 20
Contents of this Dissertation.................................................................................................. 25
REFERENCES ....................................................................................................................... 28
Chapter 1 – Impact of AHP pretreatment on corn stover and switchgrass composition, inhibitor
release and enzymatic digestibility ....................................................................................... 37
Introduction ............................................................................................................................ 37
Methods .................................................................................................................................. 41
Bench-Scale Alkaline Hydrogen Peroxide Pretreatment ............................................ 41
Scaled-up Alkaline Hydrogen Peroxide Pretreatment ................................................. 42
Enzymatic Hydrolysis.................................................................................................... 42
Composition Analysis.................................................................................................... 43
HPLC Analysis .............................................................................................................. 44
Results and Discussion .......................................................................................................... 45
Hydrogen Peroxide Effects ........................................................................................... 45
High Solids Scale-up ..................................................................................................... 53
Conclusions ............................................................................................................................ 57
Acknowledgments .................................................................................................................. 58
APPENDIX ............................................................................................................................ 59
REFERENCES ....................................................................................................................... 65
Chapter 2 – Bench scale combined diffuser type extraction and alkali pretreatment of sweet
sorghum sugars and bagasse .................................................................................................. 70
Introduction ............................................................................................................................ 70
Methods and Materials ........................................................................................................... 73
Biomass .......................................................................................................................... 73
v

LHW and Alkali Pretreatment ...................................................................................... 74
Sugar Extraction and Alkali Pretreatment .................................................................... 74
Enzymatic Hydrolysis.................................................................................................... 75
Fermentation .................................................................................................................. 75
Results and Discussion .......................................................................................................... 76
Conclusions ............................................................................................................................ 85
APPENDIX ............................................................................................................................ 86
REFERENCES ....................................................................................................................... 88
Chapter 3 – Impacts of delignification and hot water pretreatment on the water induced cell wall
swelling behavior of grasses and its relation to cellulolytic enzyme hydrolysis and
binding .................................................................................................................................... 91
Introduction ............................................................................................................................ 91
Materials and Methods ........................................................................................................... 95
Biomass .......................................................................................................................... 95
LHW Pretreatment ......................................................................................................... 95
AHP Delignification ...................................................................................................... 96
Enzymatic Hydrolysis.................................................................................................... 96
Water Retention Value .................................................................................................. 97
Settling Volume ............................................................................................................. 97
Enzyme Binding............................................................................................................. 98
Dynamic Vapor Sorption............................................................................................... 99
Potentiometric Titration................................................................................................. 99
FTIR-ATR ...................................................................................................................... 99
Results and Discussion ........................................................................................................100
Biomass Pretreatment and Delignification .................................................................100
Water Retention Value and Settling Volume .............................................................102
Enzyme Adsorption .....................................................................................................106
Dynamic Vapor Sorption.............................................................................................107
Cell Wall Carboxylate Content ...................................................................................111
FTIR-ATR ....................................................................................................................113
Conclusions ..........................................................................................................................115
Acknowledgements ..............................................................................................................116
APPENDIX ..........................................................................................................................117
REFERENCES .....................................................................................................................120
Chapter 4 – Water Retention Value of Pretreated Grasses Multiple Linear Regression Models 127
Introduction ..........................................................................................................................127
Methods and Materials .........................................................................................................132
Biomass ........................................................................................................................132
LHW Pretreatment .......................................................................................................132
AHP Pretreatment ........................................................................................................133
Enzymatic Hydrolysis..................................................................................................133
Water Retention Value ................................................................................................134
Differential Scanning Calorimetry..............................................................................134
Results and Discussion ........................................................................................................136
vi

Conclusions ..........................................................................................................................145
APPENDIX ..........................................................................................................................146
REFERENCES .....................................................................................................................153
Chapter 5 - Summary of Work, Conclusions, and Future Directions ...........................................158
REFERENCES .....................................................................................................................160

vii

LIST OF TABLES

Table 1: AHP pretreatment conditions summary of select publications ........................................ 13
Table 2: Glucan, xylan and lignin contents of untreated and AHP pretreated corn stover and
switchgrass along with glucose yields................................................................................ 46
Table 3: list of factors and levels for design of experiments statistical analysis ........................... 61
Table 4: ANOVA tables for glucose (top) and xylose (bottom) yield including all three factors 61
Table 5: Summary of groupings using Tukey’s Test for all three factors and their levels based on
95% confidence interval ...................................................................................................... 63
Table 6: Summary of groupings for glucose yield using a pairwise t-test for 48 h hydrolysis time
and select pretreatment times: 3, 9, 24 hours ..................................................................... 64
Table 7: Impact of LHW and alkali pretreatments on solubilized material during pretreatment
(removed) and glucan, xylan and lignin content of residual solids after pretreatment ... 77
Table 8: Glucan, xylan, lignin compositions and glucose yields of corn stover and switchgrass
before and after AHP delignification and LHW pretreatment ........................................118
Table 9: Variables with coded symbols and variable descriptions ...............................................147
Table 10: Selection criteria values for the four models being investigated .................................151
Table 11: Summary of t and F test for the inclusion of G and L in best model selection ...........152

viii

LIST OF FIGURES

Figure 1: U.S. energy consumption by source and sector (top), and U.S. crude oil and liquid fuel
production (bottom). Top figure published in U.S. Energy Information Administration
Annual Energy Review 2010, bottom figure published in U.S. Energy Information
Administration Short-Term Energy Outlook. ...................................................................... 2
Figure 2: The scope of this research will investigate AHP pretreatment integration with enzyme
hydrolysis and fermentation for different biomass feedstock and be used as a tool to
understand cell wall characteristics that contribute to processing effectiveness based on
digestibility and fermentability ............................................................................................. 9
Figure 3: AHP pretreated A) corn stover and B) switchgrass water sorption comparison ........... 21
Figure 4: Diagram of various cell wall properties that contribute to cell wall hydrophilicity of
biomass and ultimately enzymatic digestibility ................................................................. 22
Figure 5: Glucose and xylose hydrolysis yields as a function of A) hydrogen peroxide loading
and B) lignin removal .......................................................................................................... 47
Figure 6: Ferulic and p-coumaric acid release (solubilization) as a function of hydrogen
peroxide loading during AHP delignification .................................................................... 49
Figure 7: Correlation between A) xylan and lignin removal for corn stover and switchgrass and
between B) lignin and ferulic acid removal ....................................................................... 50
Figure 8: Fraction of solubilized xylan following AHP pretreatment and enzymatic hydrolysis
that is monomeric and oligomeric as a function of H 2O2 loading for A) corn stover and
B) switchgrass ...................................................................................................................... 51
Figure 9: Hydrogen peroxide utilization with time for 0.12 and 0.25 g/g hydrogen peroxide
loading .................................................................................................................................. 52
Figure 10: Sodium hydroxide consumption during AHP delignification for corn stover (A) and
switchgrass (B) for 2% and 8% solids concentration ........................................................ 53
Figure 11: A) glucose yield and B) xylose yield of AHP pretreated corn stover as a function of
solids loading and pretreatment time for the large scale pretreatment ............................. 55
Figure 12: Cinnamic acid released expressed as A) g/L liquid phase concentration and B) g/g
corn stover concentration as a function of solids concentration and temperature during
small scale AHP pretreatment of corn stover..................................................................... 57
Figure 13: Interaction plot for glucose yield.................................................................................... 62
ix

Figure 14: Interaction plot for xylose yield ..................................................................................... 62
Figure 15: Composition of carbohydrates, both soluble and structural, in the sweet sorghum .... 77
Figure 16: Glucose and xylose yields for LHW pretreated, alkali pretreated and untreated
bagasse.................................................................................................................................. 78
Figure 17: potential process schemes for integrating sugar extraction and bagasse pretreatment
and hydrolysis of sorghum to generate various sugar streams that can be utilized for fuel
production, or other sugar-based products ......................................................................... 80
Figure 18: selected mass balance and sugar concentrations in juice and hydrolyzate for the
combined sugar extraction/pretreatment process integrated with enzyme hydrolysis and
fermentation ......................................................................................................................... 82
Figure 19: sugar profile during extraction for each stage and pH change during the process...... 83
Figure 20: sugar utilization during fermentation of juice/hydrolysate combination using S.
cerevisiae GLBRC Y73 strain on A) combined juice and hydrolyzate and B) 6-fold
increase in concentration by rotary evaporation ................................................................ 84
Figure 21: Extraction setup showing the stage # and the direction of water/juice and
sorghum/bagasse flow ......................................................................................................... 87
Figure 22: Observable macroscopic differences in the water swelling behavior of AHPdelignified (A) corn stover and (B) switchgrass at 15% (w/v) solids content. ..............101
Figure 23: Water retention value (WRV) for corn stover (CS) and switchgrass (SG) before and
after AHP delignification as a function of centrifugation speed for 15 min centrifugation
time .....................................................................................................................................103
Figure 24: Corn stover (CS) and switchgrass (SG) correlation between the enzymatic glucose
yield and (A) settling volume and (B) WRV ...................................................................105
Figure 25: Impact of increased WRV on (A) enzyme binding and (B) the correlation between
bound enzyme fraction and WRV and enzymatic glucose yield. ...................................107
Figure 26: Water activity isotherms for untreated and AHP delignified corn stover..................109
Figure 27: The carboxylic acid content determined by potentiometric titration as a function of
AHP pretreatment condition on (A) a per mass biomass and (B) a per mass residual
lignin basis and as a function of enzymatic glucose yield on (C) a per mass biomass and
(D) a per mass residual lignin basis ..................................................................................112
Figure 28: FTIR spectra of AHP delignified corn stover compared to (A) untreated material and
to (B) LHW treated material. Results show transmittance deviation for each delignified
x

material from the untreated material or LHW-only spectra which were used as the
baselines .............................................................................................................................115
Figure 29: Fabricated Buchner funnel with 200 mesh porous base (left) and modified spin
column with 200 mesh membrane (right) ........................................................................119
Figure 30: Example of a thermogram obtained from DSC at the different melting temperatures.
The temperature regime with the heat-up and isothermal holds is shown in blue and the
heat flow shown in green ..................................................................................................136
Figure 31: Cumulative freezing bound water content as a function of pore diameter for AHP
delignified corn stover (A) and switchgrass (B) obtained from DSC melting temperature
depression endotherms ......................................................................................................137
Figure 32: Correlation of glucose yield and WRV for (A) AHP delignified corn stover and
switchgrass with linear regression model and 90% confidence interval shown in dashed
lines and (B) the linear model from graph A and data from combined LHW and AFEX
pretreatments with AHP delignification ...........................................................................139
Figure 33: Relationship between xylan and glucan content changes for AHP delignification,
AFEX, and LHW pretreatment of switchrass (arrows indicate increasing pretreatment
type intensity, which is temperature for AFEX and LHW, and H 2O2 loading for AHP)
.............................................................................................................................................140
Figure 34: Correlation between glucose yield and WRV for AHP delignified corn stover and
switchgrass (presented in Figure 32A) and AFEX pretreated corn stover and
switchgrass at two different ammonia loadings , 1.5 and 2 g/g biomass, and a range of
temperatures, 60, 90, 120, and 150 oC..............................................................................141
Figure 35: Heat map of correlation matrix for the response variable glucose yield (Y), and four
predictor variables, WRV, glucan (G), xylan (X) and lignin (L) content ......................142
Figure 36: Predicted versus actual glucose yield for two linear regression models: model 1
includes WRV only, while model 2 includes WRV and residual xylan content after
pretreatment........................................................................................................................144
Figure 37: Scatter plot matrix for the respone variable (Y, glucose yield) and all four predictor
variables..............................................................................................................................148
Figure 38: Residual vs Fitted plots, Normal plot and Leverage plot of all data ..........................149

xi

KEY TO ABBREVIATIONS

GHG: greenhouse gas
HDI: human development index
RFS: Renewable Fuel Standard
AFEX: ammonia fiber expansion
AHP: alkaline hydrogen peroxide
BSA: bovine serum albumin
HMF: hydroxymethyl furfural
DSC: differential scanning calorimetry
WRV: water retention value
LHW: liquid hot water
HPLC: high performance liquid chromatography
NREL: National Renewable Energy Laboratory
SG: switchgrass
CS: corn stover
ANOVA: analysis of variance
GLBRC: Great Lakes Bioenergy Research Center
xii

YNB: yeast nitrogen base
OD600: optical density at 600 nm
BET: Brunauer-Emmett-Teller theory
NMR: nuclear magnetic resonance
FTIR-ATR: Fourier transform infrared spectroscopy – attenuated total reflectance
DVS: dynamic vapor sorption
CBM: carbohydrate-binding module
MLR: multiple linear regression
AIC: Akaike information criteria
BIC: Bayesian information criteria
IR: infrared
PLS: partial least squares
PRESS: predicted residual sums of squares

xiii

Introduction
Motivation – Biofuels and Sustainable Energy
Fossil fuels provide 80% of energy needs worldwide, with primary sources including
coal, natural gas and petroleum (EIA, 2011). Although nonpetroleum sources of liquid fuels are
currently utilized (i.e. coal to liquid, gas to liquid, biofuels and kerogen), the overwhelming
majority of liquid fuels consumed in the U.S. come from petroleum (see Figure 1) (Outlook,
2010). According to the U.S. Energy Information Administration’s International Energy
Outlook 2013 report, the transportation sector will account for over 60% of the projected energy
demand growth worldwide in the next 30 years (EIA, 2011), and in the U.S., the transportation
sector accounts for as much as 70% of the total national petroleum consumption (see Figure 1).
As demand and price for liquid fuels increase, petroleum in particular, other sectors outside of
transportation and industrial usage will be able to use other sources of energy (EIA, 2011).
However, commerce is completely dependent on high-energy density liquid fuels and is essential
for shipping via aviation, ocean shipping and land freight (Dale & Ong, 2012), not to mention
the huge infrastructure investments within the transportation sector which alone invests upwards
of $10 billion per year just in retail infrastructure dedicated to liquid fuels, and this does not
include infrastructure required for production and distribution (Melaina et al., 2013). Therefore,
transitioning to other forms of energy storage (i.e. electric, hydrogen and other non-drop-in
liquid fuels) that can be used for transportation will be gradual, and liquid fuels will continue to
be essential for the foreseeable future, especially fuels that are compatible with the current
production, distribution and retail pipeline. Currently, the only options for renewable liquid fuels
are those produced from plant material, and the most promising sustainable source of plant
material is non-food cellulosic biomass (Dale & Ong, 2012). The sustainability of this biomass1

based liquid fuel system is very important, and sustainability is not simply limited to being
renewable.

Figure 1: U.S. energy consumption by source and sector (top), and U.S. crude oil and liquid fuel
production (bottom). Top figure published in U.S. Energy Information Administration Annual
Energy Review 2010, bottom figure published in U.S. Energy Information Administration ShortTerm Energy Outlook.

2

Sustainable energy by definition is the provision of energy to meet current demands
without compromising the ability to meet future needs. And sustainability more generally refers
to the wellbeing across the domains of environment, social equity, and economics. A truly
sustainable energy system will promote wellbeing in all three of these areas. Biofuels are a
renewable resource because they can be replenished on a reasonable time scale (every year)
compared to fossil fuels (thousands of years) by growing biomass every year, making it a more
resource responsible system compared to using petroleum (Narayan, 2001). Biofuels may also
have the environmentally friendly benefit of being carbon neutral because CO 2 released during
combustion of the fuel would be reincorporated into newly growing biomass material; therefore,
biofuels use may actually reduce net carbon emissions compared to fossil fuels. These two
environmental benefits are indicative of a sustainable system. However, whether these benefits
would be realized is still debated and the subject of much research. For example, there is the
potential issue of a net negative energy balance between fossil fuel consumption and bioethanol
production in the current biorefining processes; meaning that more energy from fossil fuels is
required to produce bioethanol than is contained in the bioethanol itself. This really means that a
bioethanol plant is more of a conversion process between fossil fuels, such as natural gas, to
ethanol. This would negate the claimed environmental benefit of a renewable biofuel system
because of fossil fuel consumption and negate the carbon neutral system it potentially has;
however, even with a net negative energy balance, the biofuel system could still reduce
dependence on fossil fuels from foreign sources. Furthermore, there is the controversial issue
that indirect land-use changes may have a significant impact on greenhouse gas (GHG)
emissions, especially in the early stages of biomass production for biofuels (Melillo et al., 2009).
This means that the initial clearing of marginal land to be used for biofuel crops could release a
3

large amount of sequestered carbon in both biomass and soil yielding a net increase is GHG
emissions. However, accurate estimates of the amounts of released carbon are still hotly
debated, and the relative importance of this released carbon is not known (Wang & Haq, 2008).
Furthermore, even if there is a benefit in CO2 emission reduction from a biofuel-based fuel
system, N2O from fertilizer may be a problem as it is likely to increase in emissions as more land
is used to grow crops for fuels, at least with our current agricultural practices (Melillo, et al.,
2009). In spite of these challenges, biofuels have a great potential to improve the environmental
sustainability of our energy production practices.
In addition to environmental sustainability, biofuels also have the potential benefit of
reducing dependence on foreign oil and producing domestic jobs for growing biofuel crops and
producing the biofuel itself. However, economic drivers are not always easy to control or predict
and there is concern of the potential exploitation of developing countries with cheap farmable
land. For example, the African continent is particularly attractive for growing biomass to be
used for biofuels because it has a lot of rain-fed land that is largely uncultivated and relatively
cheap. However, many of the African countries are poor and already have a difficult time
supplying enough food to their population, and therefore would need to be careful in regulating
food and biofuel crop production (Greiler, 2007). If done carefully, the social benefits of a
biofuel system have the potential to enhance the wellbeing of many developing countries. Bruce
Dale et. al. have said that “…commerce has been recognized as a key element in trade that
enable greater wealth and therefore greater human development” (Dale & Ong, 2012). Dale et.
al. showed a positive correlation between power consumption per capita and the Human
Development Index (HDI), which uses life expectancy, education and income to quantify the
level of development in countries and is used as a policy basis for determining a countries ability
4

to provide for its citizen’s wellbeing around the world (Dale & Ong, 2012). The clear positive
correlation between power consumption and HDI is a compelling argument that providing a
means for developing countries to produce their own liquid fuels for consumption and export has
the potential, if done right, to improve their quality of life and promote social equity.
The economic sustainability of first generation liquid biofuels, in particular ethanol, has
been shown to be tractable in Brazil and the U.S. Brazil produces 5.6 billion gallons of ethanol
from sugar cane annually and the U.S. has a production capacity of corn ethanol near 14 billion
gallons annually. The price of ethanol to produce is comparable to gasoline; in the U.S. in 2013,
the price of ethanol ranged from $1.50 to $3.30 per gallon (Babcock, 2012). However, the price
of ethanol is largely determined by fluctuations in corn (or sugar cane in Brazil) prices and
natural gas cost, which is used in production. In fact, the cost per gallon to produce ethanol from
corn is less than $1.00 depending on the price of natural gas (McAloon, Taylor, Yee, Ibsen, &
Wooley, 2000). However, over the last decade, there has been a fivefold increase in biofuel
production worldwide that has coincided with a sharp increase in food commodity prices
sparking the “food vs fuel” controversy which incriminates the use of cropland to grow biofuel
crops causing a rise in food prices (HLPE, 2013). The use of cellulosic plant material in place of
starch or sugar based plants has been proposed as a viable alternative that could circumvent the
food vs. fuel debate completely by being a cheaper source of raw material that can be grown on
less attractive, marginal land (Zilberman, Dale, Fixen, & Havlin, 2013). However,
deconstruction of cellulosic biomass to its carbohydrate components is more difficult than it is
for high sugar and starch crops such as sugar cane and corn. Therefore, there are several
conversion process challenges that require mitigation before cellulosic ethanol becomes an
economically attractive option.
5

Challenge - Cellulosic Ethanol
The production of liquid fuels from renewable biomaterials is a popular and important
topic in both ecopolitics, as discussed above, and the research world. In the U.S., legislation is in
place to promote the development of a renewable fuel industry with the hopes it will provide a
domestically sustainable source of liquid transportation fuels while also reducing dependence on
foreign oil (Gonzalez-Garcia, Moreira, & Feijoo, 2010; Schell, Riley, & Petersen, 2008). The
Environmental Protection Agency (EPA) is responsible for ensuring that transportation fuels sold
in the U.S. contain a minimum amount of renewable fuels. To do this, the Renewable Fuel
Standard (RFS) was developed under the Energy Policy Act of 2005 which mandates that an
increase in renewable fuel blended with petroleum based transportation fuels is to increase from
9 billion gallons in 2008 to 36 billion gallons in 2022. To reach this goal, the mandate includes
16 billion gallons from cellulosic and agricultural waste-based biofuels by 2022 (Alvira, TomasPejo, Ballesteros, & Negro, 2010). In 2011, only 7 million gallons of ethanol was produced from
cellulosic material of the 13.5 billion gallons of biofuels produced in that year, indicative of the
challenge that exists in order to reach the RFS mandated target for these cellulose based fuels
(Igathinathane, Pordesimo, Womac, & Sokhansanj, 2009). The current challenge is to develop
processes for converting cellulosic biomass to liquid fuels at a cost that is competitive with
starch-based processes.
In the U.S., almost all of the 13.5 billion gallons of fuel produced in 2011 from
biomaterial was ethanol, and almost all of that was produced from corn starch (only 0.3 billion
gallons was biodiesel). The process for converting cellulosic biomass to ethanol is more
challenging than the process for corn starch or other sugar rich materials due to the so called
recalcitrant nature of lignocelluloses. Recalcitrance refers to the three components of
6

lignocelluloses: lignin, hemicellulose and cellulose combining to make an ultrastructure that is
very resistant to biological degradation. Deconstructing lignocellulosic material to release the
polymerized sugars cost effectively is a primary challenge to moving this technology into an
industrially feasible process (S. Banerjee et al., 2010). One of the more compelling process
schemes to do this is the biochemical conversion platform, where enzymes are used to hydrolyze
sugar polymer bonds and release monomeric sugars that can be used by fermenting organisms to
produce fuels like ethanol or butanol (Garcia, Pakkila, Ojamo, Muurinen, & Keiski, 2011), or
commodity chemicals like lactic acid, succinic acid, and xylitol (Adsul, Singhvi, Gaikaiwari, &
Gokhale, 2011; FitzPatrick, Champagne, Cunningham, & Whitney, 2010; Saha, 2003).
However, due to the recalcitrant nature of lignocelluloses, a pretreatment step is usually required
before hydrolysis to improve enzyme access to sugar polymers and thus improve sugar polymer
saccharification. Therefore, to convert lignocelluloses to fuel ethanol requires 3 operations:
pretreatment, enzyme hydrolysis and fermentation.
The goal of pretreatment is 3 fold: increase polysaccharide accessibility by redistributing
or removing lignin and hemicelluloses, increase material porosity, and decrease cellulose
crystallinity so that enzymes in the subsequent step can access the sugar polymers to perform
their hydrolysis reactions. Pretreatment can be grouped into a few different categories: physical
pretreatments, chemical pretreatments and biological pretreatments (Cheng & Timilsina, 2011;
Hendriks & Zeeman, 2009; Yang & Wyman, 2008). Physical pretreatments include mechanical
grinding or explosive techniques such as steam explosion and ammonia fiber expansion (AFEX).
Chemical pretreatments include acid and alkali hydrolysis reactions that are typically performed
at higher temperatures (100-190 oC) as well as oxidative pretreatments such as alkaline hydrogen
peroxide (AHP). Biological pretreatments use various species of fungi to degrade lignin and
7

cellulose to make them more amenable to the enzymes in the next step (Adsul, et al., 2011). All
of these pretreatment techniques have advantages and disadvantages compared to the others
which has made it difficult for a clear front runner to emerge. Therefore, research on all of these
technologies is still worthwhile and relevant.
This work will investigate the use of alkali and AHP pretreatment both practically from a
process standpoint for producing biofuels, as well as fundamentally by examining how AHP
affects lignocelluloses and how these effects translate into improvements in enzymatic
digestibility (Figure 2). Specifically, corn stover and switchgrass, which are two popular grass
feedstocks advocated to be used at large scale in cellulosic ethanol production, will be AHP
pretreated at more industrially relevant conditions than have been done previously and
pretreatment effectiveness will be evaluated. Additionally, the impact of AHP pretreatment on
composition changes, downstream process inhibitor formation, and the water-cell wall
interactions of biomass will be investigated to gain fundamental understanding of biomass
property changes that are important for developing an economically feasible conversion process.
Following is a literature review describing what has been done previously with AHP
pretreatment and the important process conditions that impact pretreatment effectiveness and
relevant work on enzyme hydrolysis, fermentation and biomass characterization especially
concerning water – biomass interactions.

8

Figure 2: The scope of this research will investigate AHP pretreatment integration with enzyme
hydrolysis and fermentation for different biomass feedstock and be used as a tool to understand
cell wall characteristics that contribute to processing effectiveness based on digestibility and
fermentability

Background Literature Review – AHP Pretreatment, Hydrolysis and Fermentation
Research on AHP being used as a pretreatment for lignocellulosic ethanol production as
well as animal feed amelioration began as early as the 1980’s and is based on existing processes
already used in the pulp and paper industry for pulp bleaching (Gould, 1984, 1985; Gould &
Freer, 1984). Typically, bleaching is done in multiple stages, where earlier stages are
responsible for most of the delignification and later stages eliminate chromophores thereby
brightening pulp (Reeve, 1996). H2O2 bleaching is performed at solids concentrations ranging
from 12% to 30% with peroxide loadings from 1% to 4% typically performed at 90 oC
(atmospheric bleaching) for up to 6 h. However, higher temperature bleaching above
atmospheric pressure can significantly reduce the required bleaching duration (Bajpai, 2012).
Peroxide bleaching processes will sometimes have metal removing steps prior to help stabilize
the peroxide, which has been shown to then preferably remove chromophoric structures while
leaving the lignin structure intact, effectively brightening pulp without delignifying (Suchy &
9

Argyropoulos, 2002). The key difference between pulp bleaching and AHP pretreatment is the
use of a single stage in pretreatment instead of multiple as in bleaching because the primary goal
is delignification, which is largely achieved at the beginning of bleaching, and not brightening.
AHP pretreatment is done by mixing biomass with hydrogen peroxide and a strong base,
typically NaOH, at a pH of 11.5; the concentrations of biomass and H 2O2 can be varied. Studies
have shown that during pulp bleaching, H2O2 will degrade to form reactive oxygen species such
as hydroxyl radicals and superoxide ions which are responsible for the oxidation of phenolic
compounds such as lignin. The formation of these radicals is optimal at pH 11.5, which is the
pKa of H2O2 (Agnemo & Gellerstedt, 1979; Gellerstedt & Agnemo, 1980; Gellerstedt, Hardell,
& Lindfors, 1980). The degradation products of lignin can also produce quinines which,
themselves, can further increase the degradation of H 2O2 to radicals increasing the consumption
of H2O2 for the process (Agnemo & Gellerstedt, 1979; Gellerstedt, et al., 1980). Therefore, AHP
has the advantage of being a delignifying pretreatment where lignin is separated from the sugar
polymers by being solubilized or degraded. AHP has the further advantage of being performed
at room temperature and atmospheric pressure reducing energy input requirements compared to
other high temperature and pressure pretreatment technologies (Banerjee, Car, Scott-Craig,
Hodge, & Walton, 2011). Important process variables that affect AHP pretreatment performance
are H2O2 concentration, pH, temperature, pretreatment time and solids concentration. These
pretreatment variables will have impacts on the structural and compositional components of
lignocellulosic biomass that are important for the downstream performance of hydrolysis and
fermentation. The following will describe the effects of pretreatment conditions on pretreatment
performance based on these biomass characteristics and downstream processes.

10

AHP Pretreatment - pH Control
The desire to operate AHP pretreatment at pH 11.5 for reasons mentioned above presents
a process challenge in that the pH is not constant during pretreatment due to two factors: acetic
acid released from the biomass will lower pH, and the formation of hydroxide from H 2O2
decomposition will raise the pH. Gould et. al have shown that for the pretreatment conditions
they used on wheat straw, the pH will increase from 11.5 to 12 at high H 2O2 concentrations in 6
hours (Gould, 1985). However, experiments done in our lab on switchgrass and corn stover have
shown that the pH can decrease as much as 3 units or increase as much as 1 unit depending on
the H2O2 concentration, or more accurately, depending on the ratio of the H2 O2 concentration to
the biomass concentration; at a high enough ratio the hydroxide formation will be higher than
acetic acid release and the pH will increase, and at a low enough ratio hydroxide formation will
be lower than acetic acid released and the pH will decrease. The ratio at which pH migration
will change direction is likely feedstock specific and depends on acetic acid content. Not
controlling pH and performing AHP pretreatment at pH values lower than 11.5 will reduce
pretreatment effectiveness (Gould, 1985); performing at pH values higher than 11.5 will yield
better results than the lower pH pretreatments, however, this becomes an alkali pretreatment and
the beneficial effects of H2O2 are not observed. Therefore, to fully utilize the H 2O2 during
pretreatment it is important to maintain pH at 11.5 by the addition of alkali or acid during the
pretreatment process or by using a buffer.
Temperature and Pretreatment Time
As mentioned previously, AHP pretreatment is typically performed at room temperature,
or more accurately, without the addition of heat. However, the pulp and paper industry will
increase the temperature during bleaching to improve paper brightness and/or to improve H 2O2
11

effectiveness in lignin oxidation (Loureiro, Domingues, Fernandes, Carvalho, & Evtuguin,
2012). Therefore, studies have been done to investigate the effect of temperature on AHP
pretreatment performance. Saha et. al. examined the effect of temperature and pretreatment time
on pretreatment effectiveness based on sugars released after enzyme hydrolysis. The study
found that at 25 oC pretreatment improvement stopped after 6 hours, but at 35 oC there was not
much improvement after 3 hours of pretreatment for wheat straw at 9% solids (w/v) (Saha &
Cotta, 2006). Similar results were found by Rabelo and coworkers using bagasse as the
feedstock at 4% solids (w/v); for higher pretreatment temperatures, shorter pretreatment times
were required to reach maximum achievable digestibility (Rabelo, Maciel, & Costa, 2008),
which has also been known in the pulp bleaching process to be true (Bajpai, 2012). A thorough
investigation of temperature and pretreatment time is missing in the literature; performing AHP
pretreatment at higher temperatures may also affect lignin oxidation, sugar degradation and
downstream inhibitor formation.
Hydrogen Peroxide and Solids Concentrations
Much of the previous research on AHP pretreatment used solids concentrations ranging
from 1% to 10% (w/v) with the exception of one study using ~40% solids in which they used a
modified extruder to mix the high solids slurry (Gould, 1985). H 2O2 loadings typically ranged
from 10% to 50% on a wt H2O2/wt biomass basis. These conditions yielded positive results for
lignin removal and improved enzymatic digestibility for a range of biomass feedstocks. Gould
performed AHP pretreatment at relatively dilute concentrations of H 2O2, as low as 1% (w/v), and
low solids concentrations, anywhere from 1 – 10% (w/v); this study showed lignin removal as
high as 50% and enzyme hydrolysis improvements of glucose yields from 30% for untreated
material to over 90% for pretreated wheat straw and kenaf (Gould, 1984). Subsequent studies
12

using similar pretreatment conditions resulted in similar results for wheat straw, corn cob, corn
husk, alfalfa hay, soy bean stover, and others (Gould, 1984, 1985; Gould & Freer, 1984; Gould,
Jasberg, Fahey, & Berger, 1989). However, these low solids conditions are not desirable for an
industrial process which would lead to large volumes of wastewater that would require treatment,
and dilute downstream product concentrations that would have high energy requirements for
separation (Modenbach & Nokes, 2012). Therefore, higher solids concentrations during
pretreatment are essential for AHP pretreatment to be industrially viable. Numerous studied
have investigated the impact of performing pretreatment and hydrolysis at high-solids loadings
(Hodge, Karim, Schell, & McMillan, 2008; Jorgensen, Vibe-Pedersen, Larsen, & Felby, 2007;
Kristensen, Felby, & Jorgensen, 2009; Lu et al., 2010; J. Zhang et al., 2010). Table 1 shows a
summary of AHP pretreatment conditions investigated in the literature, it should also be noted
that most studies washed the biomass following pretreatment effectively removing any inhibitors
to hydrolysis, which would not be practical in an large scale process with so much water usage.

AHP Pretreatment Review

Table 1: AHP pretreatment conditions summary of select publications
Year Pub

Feedstock

H2O2 Loading

Solids Loading

Washed
Biomass

1983-85

Corn Cob, Husk, Stalk
Foxtail, Alfalfa Hay
Kenaf, Wheat Straw

30 - 50% g/g

2-6% w/v

Yes

1987

Wheat Straw

5% g/g

45% w/w

Yes

2006

Wheat Straw

50% g/g

9% w/v

No

2007

Cotton Stalk

20% g/g

10% w/v

Yes

2008

Wheat straw,
Bagasse

25 - 50% g/g

4-8% w/v

Yes

2009

Corn Stover

25% g/g

4% w/v

Yes

2011

Corn Stover

12-50% g/g

2-10% w/v

Yes

Too High

Too Low

Impractical

Better feedstock, enzymes and fermenting organisms available now
13

The first attempt at a high solids AHP pretreatment utilized an extruder, as mentioned
previously, to allow for sufficient mixing at solids concentrations as high as 40% (w/v) (Gould,
et al., 1989). The extruder was used to mix wheat straw, H 2O2, and NaOH solution for about 15
sec, and then the mixed slurry was sealed into a plastic bag for the duration of the 24 h
pretreatment time. Glucose yield improvements for the wheat straw were from 50% glucose
yield for untreated material to over 80% glucose yield for AHP pretreated material; these results
were better than those of previous studies at lower solids loadings. Under these conditions the
temperature of the slurry after exiting the extruder reached upwards of 80 oC either due to
friction of mixing or from exothermic H 2O2 degradation. The authors suggested the higher
temperature could explain the improvement of pretreatment efficacy compared to previous work,
however, they did not explain why this should be the case. Therefore, it was not clear if the
improved pretreatment performance was a result of higher solids conditions, improved mixing in
the extruder, higher temperatures or a combination of all of these.
Scale-up from the bench scale of AHP pretreatment has also been attempted in the
context of improving biomass digestibility in rumen animals. 70-90 kg of wheat straw was AHP
pretreated in large stainless steel vats at approximately 3-5% solids and 30% H2O2 loading
(Kerley, Fahey, Berger, Merchen, & Gould, 1987; Meeske, Meissner, & Pienaar, 1993).
However, because the biomass was used as animal feed, comparing the results of pretreatment
performance based on enzyme hydrolysis is not possible. In any case, the H 2O2 conditions and
solids concentrations used in these studies are too high and too dilute respectively to be
industrially feasible. Banerjee et al performed AHP pretreatment on 1 kg of corn stover using
15% (w/v) solids and 12.5% H2O2 loading and were able to achieve glucose yields upwards of

14

75%; this demonstrated for the first time that AHP scale-up can be done reproducibly with good
results and at much more industrially relevant conditions (Banerjee et al., 2012).
Gould and coworkers also showed in early studies of AHP pretreatment that much of the
H2O2 degrades to oxygen and water, indicating unutilized potential for lignin oxidation (Gould,
1985). The incorporation of oxygen into biomass by oxidation was evaluated by measuring the
O2 evolved during pretreatment; the difference in measured O 2 evolution from the theoretical
amount that should have been evolved based on stoichiometry was considered to be incorporated
into the biomass. For the AHP conditions they used on kenaf at a solids concentration of 2%
(w/v), they found ~20% H2O2 incorporation. They also found that improvement of
incorporation, to ~40%, was achieved simply by performing pretreatment at a higher solids
concentration and further, that the initial rate of O 2 evolution was increased at this higher solids
concentration (6% w/v). These results are comparable to results observed in our lab using corn
stover where we measured ~20% H2O 2 incorporation. This indicates there is a lot of room for
improvement of H2O2 utilization; perhaps even higher solids loadings can increase utilization,
but to what extent is not certain. Another possible avenue is using catalysts to focus oxidization
on desirable biomass reactions instead of with H 2O2 degradation products (Z. Li et al., 2012).
Biomass Structural and Compositional Changes
After biomass has been pretreated, understanding the structural and compositional
changes that take place and how they relate to improved enzyme digestibility is important.
Composition refers to the constituent content including glucan, xylan, and lignin as well as minor
components such as acetate, proteins and ash. How these components combine together are the
structural characteristics and include degree of polymerization, lignin-carbohydrate complexes,
cellulose crystallynity, lignin location, surface area and hornification (Kristensen, Thygesen,
15

Felby, Jorgensen, & Elder, 2008; Spinu, Dos Santos, Le Moigne, & Navard, 2011). AHP
pretreatment impacts all of these factors which in turn impacts enzymatic digestibility. It is also
worth noting that structural and compositional changes can occur during feedstock storage and
transportation (Inman, Nagle, Jacobson, Searcy, & Ray, 2010).
Gould and coworkers examined the structural effects of AHP on cellulose using Xray
diffraction and found that AHP “loosens” the three dimensional structure of lignocelluloses by
removing (solubilizing) lignin and hemicellulose, but it has no effect on the crystallinity of
cellulose (Martel & Gould, 1990). The removal of lignin and hemicellulose are the primary
compositional components affected by AHP pretreatment, while cellulose is largely unchanged
and remains insoluble. There is also a significant fraction, 10-30%, of other extractable material
that is solubilized during AHP pretreatment that include proteins, acetate, ash and others. The
alkali in AHP is responsible for saponification reactions that cleave acetate bonds on the
hemicelluloses which then increases their solubility (Kerley, Fahey, Berger, Gould, & Baker,
1985; Pedersen & Meyer, 2010); as much as 20% of hemicellulose has been shown in the
literature to solubilize for AHP pretreatment of rye straw (Fang, Sun, & Tomkinson, 2000; Girio
et al., 2010), and in our lab hemicellulose removal has been observed between 20% and 40%
depending on pretreatment conditions and feedstock. Lignin solubilization is due to H 2O2
oxidation reactions evinced by the minimal amount of lignin solubilized under alkali only
conditions (Janker-Obermeier, Sieber, Faulstich, & Schieder, 2012). It has been suggested that
in grasses, which have high phenolic hydroxyl contents, destruction of alkyl-aryl ethers would
increase lignin solubilization during AHP pretreatment (M. Y. Li et al., 2012). The removal of
hemicelluloses and lignin components leads to an improvement in cellulose digestibility
suggesting that lignin and hemicellulose synergistically occlude cellulose from cellulase
16

enzymes. This was shown by Selig and coworkers using corn stover at 4% solids (w/v) where
AHP pretreatment was performed at different temperatures to remove different amounts of
lignin. They found by adding different mixtures of cellulose and hemicellulose degrading
enzymes that lignin is not solely responsible for blocking cellulose from enzymes and that there
is some lignin-hemicellulose interaction that together inhibits the enzymes (Selig, Vinzant,
Himmel, & Decker, 2009).
Enzymatic Hydrolysis
Following pretreatment, sugar polymers are hydrolyzed using enzymes to release
fermentable, monomeric sugars. Because of the heterogeneous composition of lignocellulosic
biomass, a system of enzymes is required which work synergistically to break bonds and expose
more enzyme starting locations for hydrolysis and to reduce product inhibition. The primary
enzyme activities required for lignocellulose hydrolysis are glucanase enzymes and xylanase
enzymes; improved digestibility can be achieved by including accessory enzymes with activities
toward minor components of lignocellulosic biomass such as glucuronidases and arabinosidases
as well as pectinases. These accessory enzymes have been shown to improve sugar yields for
glucose and xylose at relatively low fractions of the enzyme cocktail (~20%) (Banerjee et al.,
2010). Some of the key enzyme related factors that limit efficient hydrolysis include: a) product
inhibition, b) unproductive binding of cellobiohydrolases to cellulose, c) hemicellulose and
lignin association with cellulose that blocks enzyme activity, d) enzyme adsorption to lignin, and
e) loss of enzyme activity due to denaturation, mechanical shear, proteolytic activity or low
thermal stability (Jorgensen, Kristensen, & Felby, 2007). In addition to these, there are substrate
related factors that hinder enzyme hydrolysis as well including lignin composition, cellulose
crystallinity, available surface area, porosity, cell wall thickness, and changes in accessibility
17

during hydrolysis (Alvira, et al., 2010). Furthermore, there are process-condition-related factors
that impact enzymatic hydrolysis such as enzyme loading, solids concentration, and hydrolysis
time. All 3 of these groups of factors can be interrelated in regards to impact on digestibility; for
example, Zhu et. al. showed that for short hydrolysis times, lignin content did not matter when
cellulose crystallinity was low, and for longer hydrolysis times, crystallinity did not matter when
lignin content was low (Rollin, Zhu, Sathitsuksanoh, & Zhang, 2011; Zhu, O'Dwyer, Chang,
Granda, & Holtzapple, 2008). Lignin inhibits enzymes in 3 different ways: 1) physically
blocking enzyme access to sugar substrates, 2) unproductively and irreversibly binding enzymes,
and 3) forming inhibitory compounds during pretreatment (Sewalt, Glasser, & Beauchemin,
1997). Cellulase enzymes have been shown to have stronger binding affinities for lignin than for
cellulose substrates. It has also been shown that the enzymes can denature on the surface of
lignin suggesting that lignin somehow destabilizes the enzymes, and that this destabilization
increases with temperature (Rahikainen et al., 2011). Studies have also found that the severity of
lignin-enzyme binding is different depending on the pretreatment method used. For example,
acid pretreatments show more enzymatic deactivation due to lignin binding than hot water
pretreatment suggesting that the physicochemical properties of lignin are different after the 2
methods and there subsequent binding capabilities are different also (Kristensen, Borjesson,
Bruun, Tjerneld, & Jorgensen, 2007; Rahikainen, et al., 2011). The lignin binding effect has
been shown to be reduced by the addition of surfactants or bovine serum albumin (BSA) that act
as lignin blockers preventing enzymes from binding; alternatively, higher enzyme loadings
would have the same effect by sacrificing a fraction of enzymes for lignin binding (Eriksson,
Borjesson, & Tjerneld, 2002; Kristensen, et al., 2007; Yang & Wyman, 2006; Y. Q. Zhang, Xu,
Zhang, & Li, 2011).

18

In order for the biochemical conversion of lignocelluloses to ethanol to be economically
viable, both pretreatment and enzymatic hydrolysis steps will need to be performed at high solids
concentrations to reduce reactor volumes and wastewater streams. This will also allow for
ultimately higher sugar concentrations and thus higher ethanol concentrations which would
reduce the energy requirements for downstream separation. However, high solids hydrolysis
presents several challenges including higher sugar concentrations that lead to product inhibition,
higher inhibitor concentrations, mass transfer limitations, and difficulty mixing. The solids
effect is a catch-all term referring to a decrease in enzymatic digestibility at higher solids
concentrations (Kristensen, et al., 2009).
Fermentation Inhibitors
Much of the work examining the effectiveness of AHP pretreatment uses enzyme
hydrolysis sugar yield as the rubric for comparison. However, fermentability of hydrolyzate is
another important factor to consider for determining pretreatment effectiveness and for
comparing different pretreatment technologies. However, this approach is problematic since
different fermenting species and strains may be more suitable for different pretreatment liquors
and subsequent hydrolyzates. Using fermentability as a measure of pretreatment effectiveness
complicates the comparisons that are made. However, quantifying known fermentation
inhibitors after pretreatment and hydrolysis is still important. Certain compounds are known to
be inhibitive to fermenting organisms and have been shown to be present after AHP
pretreatment. Qureshi and coworkers have shown that the sodium concentrations reached during
AHP pretreatment are inhibitory to the acetone-butanol-ethanol fermenting organisms
Clostridium acetobutylicum and Clostridium beijerinckii (Qureshi, Saha, Hector, & Cotta, 2008).
In addition, weak acids, furan derivatives and phenolic compounds are known to inhibit
19

fermentation (Luo, Brink, & Blanch, 2002; Palmqvist & Hahn-Hagerdal, 2000a; Persson et al.,
2002). Weak acids (such as acetic, formic and levulinic acids) in undissosiated form are
liposoluble and can transport across the cell membrane where they can dissociate depending on
pKa and intracellular pH. This internal acidification in addition to other acid interactions with
cellular processes can inhibit fermentation (Pampulha & Loureirodias, 1990). Hydroxymethyl
furfural (5-HMF) and furfural inhibits glycolytic enzymes and contributes to the accumulation of
acetaldehyde which has been suggested as the reason for growth inhibition in the presence of
furfural (Palmqvist & Hahn-Hagerdal, 2000b). The mechanisms of phenolic inhibition to
fermenting organisms is not well understood but may be due, in part, to partitioning in cell
membranes damaging membrane selectivity (Heipieper, Weber, Sikkema, Keweloh, & Debont,
1994; Palmqvist & Hahn-Hagerdal, 2000b). The inhibitor compounds and concentrations vary
with biomass feedstock, pretreatment technology and pretreatment conditions. Detoxification of
hydrolyzates prior to fermentation by phenolic compound removal using laccase treatment and
anion exchange techniques has been shown to remove as much as 80% of phenolics (Palmqvist
& Hahn-Hagerdal, 2000b).
Cell Wall and Water Interactions
The interaction of water with biomass material during pretreatment and hydrolysis can be
an important factor for processing, especially for enzymatic hydrolysis. Figure 3 shows the
difference in water sorption between AHP pretreated corn stover and switcgrass; both materials
were pretreated at the same solids concentration (15% w/v), however, there is essentially no free
water in the corn stover sample, while the switchgrass sample is much less absorbent and has
more free water. Knowing that corn stover has a higher enzymatic digestibility than switchgrass
led to the hypothesis that this biomass-water interaction may be a useful indicator for how
20

susceptible a material is to hydrolyzing enzymes. Figure 4 illustrates the idea that certain cell
walls properties such as lignin content and cellulose crystallinity contribute to cell wall
hydrophilicity which in turn can impact water penetration and thus enzyme penetration into the
cell wall ultimately impacting enzymatic digestibility.

Figure 3: AHP pretreated A) corn stover and B) switchgrass water sorption comparison

For woody biomass, it is known that water will sorb to specific sites, hydroxyl and
carboxyl groups, in a layered or clustered formation (Berthold, Olsson, & Salmen, 1998; Froix &
Nelson, 1975; Olsson & Salmen, 2004). For some materials, 1-1.3 water molecules can sorb to a
single hydroxyl group (Olsson & Salmen, 2004). Water will interact first with readily available
hydroxyl groups, and then as the material swells, more hydroxyl groups become exposed
allowing for further water adsorption to these groups (Berthold, et al., 1998; Froix & Nelson,
1975). Water bound to cellulose primarily occurs in amorphous regions, and it has been shown
that bound water decreases with increasing crystallinity (Hatakeyama, Nakamura, &
Hatakeyama, 2000; Nakamura, Hatakeyama, & Hatakeyama, 1981) and the amorphous regions
are removed easily at the beginning of hydrolysis (Vyas, Pradhan, Pavaskar, & Lachke, 2004).
The diffusion of enzymes and hydrolysis products through amorphous regions is higher than
21

crystalline regions, which explains, in part, why hydrolysis rates are slower at later stages of
hydrolysis when most of the remaining cellulose is more crystalline. However, cellulose and
lignin do not contribute as much to water binding as hemicelluloses and pectins, likely due to
higher amounts of hydroxyl, carboxyl and other charged groups in hemicellulose components of
biomass (Lund, Sjostrom, & Brelid, 2012; Weber, Kohlhepp, Idouraine, & Ochoa, 1993).

Lignin Content
Property: Cell
Wall Composition

Polysaccharide
Content

Accessible vs.
Inaccessible
Amorphous vs.
Crystalline

Property: Cell Wall
Porosity

Property: Cell Wall
Hydrophilicity
Impacts to:
Water Penetration
Enzyme Penetration
Enzymatic Digestibility

Figure 4: Diagram of various cell wall properties that contribute to cell wall hydrophilicity of
biomass and ultimately enzymatic digestibility

Different types of feedstock, feedstock composition and even plant cell type can lead to
different water sorption behavior. For example, it has been shown that guaiacyl lignin can
restrict fiber swelling more so than syringyl lignin, therefore, differences in the amounts of these
two lignins would certainly lead to different water sorbing behavior (Ramos, Breuil, & Saddler,
1992). In addition to differences between plant species, there are differences in water sorption
within plants themselves. Igathinathane et al, examining water sorption behavior in different
locations in the plant, found that the stalk pith region of corn stover did not hold as much water
as other areas such as stalk leaf perhaps due to entrained air preventing water penetration.
Furthermore, different regions of the stover would equilibrate with water differently depending
on properties (Igathinathane, Womac, Sokhansanj, & Pordesimo, 2005). Furthermore, certain

22

regions of corn stover have been shown to have different energy states such that water binding
sites will fill up preferentially (Igathinathane, Womac, Sokhansanj, & Pordesimo, 2006, 2007).
Absorbed water in fibers is classified into three groups: water of constitution, imbibed
water and free water. Water of constitution is strongly held by the fiber surface by hydrogen
bonds and forms a monolayer on the surface. Imbibed water is the additional water held by fiber
when the relative water vapor is increased to 100%. And free water is that which is held after
reaching fiber saturation (Akinli-Kogak, 2001). Water of constitution and imbibed water are
also termed non-freezing and freezing water respectively based on their behavior during
differential scanning calorimetry (DSC) at below freezing temperatures; non-freezing water will
not freeze at temperatures well below 0 oC, and freezing water will have different freezing
temperatures below 0 o C depending on the size of the pore in which it is trapped (Kaewnopparat,
Sansernluk, & Faroongsarng, 2008). Because of this, freezing water quantification using DSC
allows for the quantification of pore size distribution. Yu et al showed that delignification of
wood will tend to increase pore volume to a certain point, quantified by freezing water with
DSC. They also showed a correlation between increasing freezing water, and thus accessible
pore volume, and carbohydrate digestibility; however, the pattern varied depending on material
and delignification technique (Yu, Jameel, Chang, & Park, 2011). Similar results have been
shown correlating pore volume with digestibility where pore volume was determined using a
solute exclusion technique; they found that larger total accessible pore volume allowed for faster
initial hydrolysis rates (Grethlein, 1985).
Several methods have been developed to quantify biomass and water interactions which
are used as important indicators of fiber properties. The water retention value (WRV) (Maloney,
Laine, & Paulapuro, 1999), settling volume (Alince & Robertson, 1974) and water activity
23

(Fardim, Liebert, & Heinze, 2013) are examples of such metrics that have standardized methods
for quantifying the amount of pore water versus free water, fiber stiffness, and water sorption
respectively. The WRV is a centrifugation method where a pad of wet biomass is drained using
centrifugation at a specified speed and duration; the value is determined by the amount of water
remaining in the biomass after centrifugation (Scallan & Carles, 1972). The settling volume
provides a metric for distinguishing differences between inter-fiber friction, and chemical
treatments that increase settling volume also tend to increase dewatering rates (Hubbe &
Heitmann, 2007). The settling volume quantifies the relative height of biomass to total slurry
height when it is allowed to settle in a solution of water for a specified solids concentration and
duration. Water activity is another method for quantifying the interaction of water with biomass
and is typically used in the food industry as a way to predict bacterial growth on food by
quantifying the amount of water absorbed in food that is available for bacterial utilization leading
to growth and food spoilage (Berg & Bruin, 1978). This metric is based on the energy state of
water bound or trapped in a solid material based on the partial pressure of water molecules
contained in the porous solid material.
For the context of this work, biomass hydrophilicity will include water association with
biomass due to spatial confinement within the biomass matrix (macro-scale), within nano-scale
pores, and physicochemical association with the solid fiber surfaces. Several cell wall properties
can contribute to changes in hydrophilicity. For example, delignification and hemicellulose
removal have been shown to increase pore volume and leave behind empty space for water
molecules to occupy, which allows for increased water penetration and thus water swelling
(Akinli-Kogak, 2001; Grethlein, 1985). Water molecules also adsorb to biomass surfaces
through polar groups, specifically hydroxyl and carboxyl functional groups (Olsson & Salmen,
24

2004). Cell wall composition, specifically lignin and hemicellulose content, can both contribute
to cell wall hydrophilicity, and pretreatment impacts composition and therefore swelling
behavior (Grabber, Hatfield, & Ralph, 2003). Polysaccharide accessibility and crystallinity can
impact cell wall hydrophilicity due to water access to cell wall surfaces and cellulose swelling;
these properties are also known to be impactful to enzymatic digestibility (Chundawat et al.,
2011). Also, cell wall porosity will be indicative of water and enzyme penetration and therefore,
perhaps, enzymatic digestibility. The hypothesis is therefore, that quantifiable water and biomass
interactions may incorporate impacts of composition, porosity and hydrophilic properties of the
cell wall and be a more useful indicator of enzymatic digestibility than any one group of these
properties on their own.
Much of the work examining water-cell wall properties has focused on woody biomass
which is more commonly used in the pulp and paper industry; water sorption behavior of grasses
is largely missing in the literature and would be a valuable addition to the breadth of knowledge
for biofuel production from this type of feedstock. Specifically, how water sorption behavior
impacts process operations of pretreatment and hydrolysis and, as this work will show, how
water sorption can be used as an indicator of pretreatment effectiveness and a predictor of
enzymatic digestibility.
Contents of this Dissertation
Chapter 1 will show results of AHP pretreatment on two different feedstock, corn stover
and switchgrass, investigating the impact of H 2 O2 loading, solids loading, pretreatment time and
scale on pretreatment effectiveness determined by sugar yields following enzymatic hydrolysis,
and on changes to biomass composition and inhibitor release and solublization. Understanding

25

the reaction space of these pretreatment conditions will allow for further optimization of AHP
pretreatment as a process.
Chapter 2 will discuss the potential for a novel combined sugar extraction and alkali
pretreatment process of sweet sorghum that could be used for biofuel production. Soluble,
readily fermentable sugars of sweet sorghum are easily extracted with hot water, however,
structural polysaccharides are unutilized and left intact after this process. Performing an alkali
extraction to remove soluble sugars and pretreat bagasse, which can then be enzymatically
hydrolyzed, would provide an additional sugar stream for fermentation. This work will present
results for this combined process concerning sugar extraction efficiency and pretreatment
efficacy based on hydrolysis yield and show fermentability of the combined extraction juice and
hydrolyzate.
In chapter 3, the impact of AHP pretreatment and liquid hot water (LHW) pretreatment
on the hydrophilic properties of biomass will be investigated, specifically WRV, settling volume,
and water activity and how these properties relate to certain composition or structural properties
such as carboxylic acid content and surface charges as well as to enzymatic hydrolysis and
enzyme binding. It will be shown that for the conditions of AHP and LHW pretreatment used
here, cell wall swelling increases with increasing pretreatment severity, and that swelling
quantified by WRV and settling volume linearly correlate well with enzyme binding and glucose
yield. Therefore, water swelling of biomass may be a useful indicator of pretreatment
effectiveness and enzymatic digestibility.
In chapter 4, a continuation of the work presented in chapter 3 will be discussed where an
even larger range of AHP and LHW pretreatments as well as results for AFEX pretreatment are

26

performed on corn stover and switchgrass, to determine if the strong linear correlation between
WRV and glucose yield is universal across these materials. It will be shown that the linear
correlation does not hold for the more extreme pretreatment conditions and for the AFEX
pretreated material when regressed with the AHP and LHW pretreated material. However, by
performing multiple linear regression (MLR) that includes composition information, for example
glucan content, as well as WRV, a much better linear predictive model can be achieved for the
conditions used here.

27

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28

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Chapter 1 – Impact of AHP pretreatment on corn stover and switchgrass composition,
inhibitor release and enzymatic digestibility
Introduction
Cellulosic biomass is a promising source of renewable material that can be used to
produce fuels such as ethanol or butanol (Garcia, Pakkila, Ojamo, Muurinen, & Keiski, 2011), or
commodity chemicals such as lactic acid, succinic acid, and xylitol (Adsul, Singhvi, Gaikaiwari,
& Gokhale, 2011; FitzPatrick, Champagne, Cunningham, & Whitney, 2010; Saha, 2003).
Deconstructing lignocellulosic material to release polymerized sugars cost effectively is a
primary challenge to moving cellulosic fuel production technology into an industrially feasible
process (S. Banerjee et al., 2010). One of the more compelling process schemes to do this is the
biochemical conversion platform, where enzymes are used to hydrolyze sugar polymer bonds
and release monomeric sugars that can be used by fermenting organisms to produce the desired
products. However, due to the recalcitrant nature of lignocelluloses, a pretreatment step is
usually required before hydrolysis to improve cell wall polysaccharide accessibility to enzymes
in order to facilitate enzyme catalysis and ultimately cell wall deconstruction to soluble sugar
monomers. Within this pretreatment step it is necessary to increase polysaccharide accessibility
by removing or redistributing lignin, increasing cell wall porosity and decreasing cellulose
crystallinity. There are many pretreatment technologies to do this and they can be grouped into a
few different categories including physical, chemical and biological pretreatments (Cheng &
Timilsina, 2011; Hendriks & Zeeman, 2009; Yang & Wyman, 2008). Physical pretreatments
include mechanical grinding and particle size reduction or explosive techniques such as steam
explosion and ammonia fiber expansion (AFEX). Chemical pretreatments include acid and
alkali hydrolysis reactions that are typically performed at higher temperatures (100-190 oC) as
37

well as oxidative pretreatments such as alkaline hydrogen peroxide (AHP) which oxidize lignin,
and to some extent carbohydrates. Biological pretreatments use various species of fungi to
degrade the lignin and cellulose to make it more amenable to the enzymes in the next step
(Adsul, et al., 2011). All of these pretreatment techniques have advantages and disadvantages
depending on the criteria of evaluation (Dale & Ong, 2012), and research in many of these
pretreatment technologies is still worthwhile and relevant.
Research on AHP being used as a pretreatment for lignocellulosic ethanol production is
based on existing processes already used in the pulp and paper industry for pulp bleaching
(Gould, 1984, 1985; Gould & Freer, 1984), and has been investigated for both application in fuel
production and animal feed amelioration (Gould, 1984; Kerley, Fahey, Berger, Merchen, &
Gould, 1987). Pulp bleaching is a multiple stage delignification and brightening process, and in
H2O2 bleaching, earlier stages are responsible for most of the delignification, while later stages
eliminate chromophores which results in pulp brightening (Reeve, 1996). H 2O2 bleaching is
performed at solids concentrations ranging from 12% to 30% with peroxide loadings from 1% to
4% at 90 oC (atmospheric bleaching) for up to 6 h, however, higher temperature bleaching
performed above atmospheric pressure can significantly reduce the required bleaching duration
(Bajpai, 2012). Peroxide bleaching processes will sometimes have prior metal removing steps to
help stabilize the peroxide, which has been shown to then preferably remove chromophoric
structures while leaving the lignin structure intact, effectively brightening pulp without
delignifying (Suchy & Argyropoulos, 2002). Therefore, key difference between pulp bleaching
and AHP pretreatment, besides the operation difference that bleaching uses multiple stages and
pretreatment is typically one, is that the primary goal of bleaching is pulp brightening, which

38

occurs in later stages, and the goal of pretreatment is delignification, which occurs at early stages
where brightening is minimal.
Therefore, AHP pretreatment is done by mixing biomass with hydrogen peroxide and a
strong base, typically NaOH, at a pH of 11.5 where the oxidant acts as a delignifying agent rather
than a brightening agent. Studies have shown that during pulp bleaching, H 2O2 will degrade to
form hydroxyl radicals and superoxide ions which are the reactive agents responsible for the
oxidation of phenolic compounds such as lignin; the formation of these radicals is optimal at pH
11.5, which is the pKa of H 2O2 (Agnemo & Gellerstedt, 1979; Gellerstedt & Agnemo, 1980;
Gellerstedt, Hardell, & Lindfors, 1980). Maintaining the pH at 11.5 during the process is
important because pH will increase or decrease depending on the H 2O2 concentration used and
the biomass being pretreated (Gould, 1985); this is likely due to the acetic acid content of the
biomass which is released during pretreatment, the oxidation of cell wall polymers to organic
acids, and the generation of hydroxyl anions from H2O2 degradation. Not controlling pH and
performing AHP pretreatment at pH values lower than 11.5 will reduce pretreatment
effectiveness (Gould, 1985). Performing pretreatment at pH values higher than 11.5 can yield
better results than those performed at lower pHs. However, this becomes an alkali pretreatment
and the beneficial effects of H2O2 are not observed (Li, Chen, Hegg, & Hodge, 2013).
Therefore, to fully utilize H2O 2 during pretreatment it is important to maintain pH at 11.5 by the
addition of alkali, or acid depending on the direction of pH migration, during the pretreatment
process. The degradation products of lignin can also produce quinones which, themselves, can
further increase the degradation of H 2 O2 to radicals increasing the consumption of H2O2 for the
process (Agnemo & Gellerstedt, 1979; Gellerstedt, et al., 1980). Thus, AHP has the advantage
of being a delignifying pretreatment where lignin is separated from the sugar polymers by being
39

solubilized or degraded. In pulp bleaching the temperature is increased above ambient
temperature to improve H2O2 effectiveness in lignin oxidation (Loureiro, Domingues, Fernandes,
Carvalho, & Evtuguin, 2012). However, AHP as a pretreatment has typically been performed at
room temperature and atmospheric pressure reducing energy input requirements (Banerjee, Car,
Scott-Craig, Hodge, & Walton, 2011). Important process variables that affect AHP pretreatment
performance are H2O2 concentration, pH, temperature, pretreatment time and solids
concentration. These pretreatment conditions will have impacts on the structural and
compositional components of lignocellulosic biomass that are important for the downstream
performance of hydrolysis and fermentation.
Grasses such as corn stover, switchgrass, miscanthus, wheat straw, and sugar cane
bagasse are examples of attractive sources of lignocelluose for biofuel production because they
are either agricultural waste products or designated energy crops that would minimize
competition with or disruption of food crop markets. Grassses are particularly susceptible to
alkali pretreatments because their cell walls contain ferulic acid ether-linked to lignin as well as
higher free phenolic contents of lignin that make them highly soluble in alkali (Lapierre, Jouin,
& Monties, 1989). Lignin content has been shown to be negatively correlated to glucan
digestibility for grasses with a range of lignin properties, indicative of the contribution lignin has
toward recalcitrance (M. Y. Li et al., 2012). However, the relative contribution of different cell
wall components to recalcitrance is not universally the same. For example, DiMartini et. al.
showed that xylan in switchgrass contributed more to recalcitrance than xylan in popular, while
lignin in poplar contributed more than in switchgrass (DeMartini et al., 2013). Lignin, xylan,
and ferulate removal in grasses (M. Y. Li, et al., 2012) are important outcomes of AHP
pretreatment and are linked to the digestibility of the cell walls. We hypothesize that xylan and
40

lignin removal are correlated for grasses following AHP pretreatment, and that removal of both
components is synergistic. For this work we will investigate the compositional changes that take
place during AHP pretreatment of corn stover and switchgrass under different hydrogen peroxide
conditions and relate these changes to sugar yield. Specifically, lignin and xylan removal will be
correlated to each other and to ferulic acid removal. In addition, AHP pretreatment will be
scaled-up from the bench scale, 8 g of biomass, to 1kg of corn stover and performed at higher
solids concentrations (>15% w/w) to evaluate potential pretreatment improvement at these more
industrially relevant conditions.
Methods
Bench-Scale Alkaline Hydrogen Peroxide Pretreatment
Pretreatment of corn stover and switchgrass was performed using four different loadings
of H2O2 on biomass: 0%, 6%, 12.5% and 25% (g H 2O2/g biomass). The pH was maintained at
11.5 by the periodic addition of aliquots of 5 M sodium hydroxide. All four pretreatment
conditions were performed in duplicate using 8g of biomass (dry basis) at 15% solids (w/v)
which is equivalent to 12.6% to 13% (w/w) depending on the H 2O2 condition. Samples were
prepared in 250 mL Erlenmeyer flasks and placed in an incubator at 300C with shaking at 180
rpm. The flasks were covered with parafilm to prevent evaporation and to allow for expansion as
the pressure in the flasks increased with O2 evolution. Pretreatment was stopped at 24 h by
diluting the sample to approximately 10% solids (w/w) with water and adjusting the pH to 4.8
using concentrated sulfuric acid in preparation for enzymatic hydrolysis.

41

Scaled-up Alkaline Hydrogen Peroxide Pretreatment
Pretreatment of corn stover was scaled-up from the bench scale using an industrial bowl
mixer to pretreat 1 kg of biomass. Pretreatment was performed using 12.5% H 2O2 loading (g
H2O2/g biomass) at pH 11.5 and solids concentrations of 15%, 25%, 35%, 45% and 55% (w/v)
which is equivalent to 12.7%, 19.2%, 24.7%, 29.3%, and 33.2% (w/w) respectively. The
appropriate amount of water, 30% H2O2 (v/v) solution and 5 M sodium hydroxide solution were
added to the bowl mixer followed by the 1 kg of corn stover and the slurry was mixed well for
several minutes. For the first 30 min the slurry would periodically be mixed as needed to prevent
the contents from overflowing the mixing bowl as the oxygen evolution caused the slurry to rise.
The pH was periodically adjusted back to 11.5 using appropriate amounts of 5 M sodium
hydroxide at 3, 6, 9, 12, and 24 h with thorough mixing; samples were also collected at these
times in duplicate and prepared for enzymatic hydrolysis by diluting with water to approximately
10% solids (w/w) and adjusting the pH to 4.8 using concentrated sulfuric acid.
Enzymatic Hydrolysis
Hydrolysis of all corn stover and switchgrass samples was performed by diluting the
AHP delignification slurries with water to 10% (w/w) solids and adjusting the pH to
approximately 4.8 using concentrated sulfuric acid as mentioned above. Then an aliquot of 1 M
citric acid buffer was added to give a concentration of 50 mM citric acid buffer in the sample
flasks. Tetracycline and cyclohexamine were added to make a concentration of 10 mg/L each to
prevent microbial contamination. Next, an enzyme mixture of Accellerase 1000, Multifect
Xylanase and Pectinase was added in a protein mass ratio of 4.4:1.7:1, respectively, at an
enzyme loading of 30 mg enzyme/g glucan; this optimized enzyme ratio was determined by
Banerjee et al. (G. Banerjee et al., 2010) and the protein contents of the enzymes were based on
42

the Bradford assay. Samples were then mixed well and placed in a shaking incubator at 50°C
with 180 rpm shaking for seven days. Sugar concentrations in the hydrolysate were determined
by HPLC using the method described in the NREL / TP 510-42618 protocol and converted to
glucose yields based on the solids content in the reaction vessel and glucan content in the
undelignified biomass.
Scaled-up pretreated corn stover hydrolysis samples were prepared by removing 150 g,
100 g, 75 g, 70 g and 60 g of pretreatment slurry from the mixing bowl for the 15%, 25%, 35%,
45% and 55% (w/v) solids conditions respectively; these samples were then diluted to 10%
(w/w) solids and the pH was adjusted to 4.8 using concentrated sulfuric acid as mentioned above.
Then 75 g of the diluted slurry was transferred to shake flasks in duplicate where citrate buffer,
tetracycline, cyclohexamide and the enzymes were added as described above. The flasks were
then placed in an incubator at 50o C with 180 rpm shaking for 48 hours with samples taken
periodically for HPLC analysis.
Composition Analysis
Composition of solid biomass before and after pretreatment, as well as after enzymatic
hydrolysis, was determined using a modified version of the NREL / TP 510-42618 two stage
acid protocol for quantifying structural carbohydrates and lignin in biomass (B. H. A. Sluiter, R.
Ruiz, C. Scarlata, J. Sluiter, D. Templeton, and D. Crocker, 2010) with minor modifications as
described by Li et al. (M. Li et al., 2012). Briefly, quantities of biomass, 72% sulfuric acid and
water were reduced to 1/3 the amounts specified in the protocol to allow for the use of smaller
volume pressure tubes for autoclaving. Thus 0.1 g of washed biomass was added to a pressure
tube along with 1 mL of 72% acid and placed in a 30 0C water bath with periodic stirring for 1 h.
Then 28 mL of water was added and the tubes were autoclaved at 120 0C for 1 hour. Contents of
43

the tube were filtered through Whatman 54 filter paper and the solids were dried in an oven at
1050C and weighed to determine Klason lignin content. The liquid fractions were prepared for
HPLC analysis and sugar concentrations were determined using an Aminex HPX-87H column
from BioRad according to the NREL procedure.
The composition of the hydrolyzate following pretreatment and enzymatic hydrolysis was
determined by using the NREL/TP-510-42623 protocol for the determination of sugars,
byproducts, and degradation products in liquid fraction process samples (B. H. A. Sluiter, R.
Ruiz, C. Scarlata, J. Sluiter, and D. Templeton, 2006). Briefly, the hydrolyzate was centrifuged
at 4500 g for 15 min and the liquid decanted. Then 3 mL of liquid sample was added to a
pressure tube along with 0.105 mL of 72% sulfuric acid; then the tubes were autoclaved at 120 0C
for 1 hour. The liquid contents of the tube were prepared for HPLC analysis and sugar
concentrations were determined using an Aminex HPX-87H column from BioRad according to
the NREL procedure. The difference in sugar contents between the liquid composition samples
and the enzymatic hydrolysis samples is the amount of solublized oligomeric carbohydrates in
the hydrolyzate.
HPLC Analysis
Glucose and xylose concentrations were determined by HPLC using an Aminex HPX87H column (#125-0140, Bio-Rad, Hercules, CA) operating at 65 oC, using a 0.05 M sulfuric
acid mobile phase, at a flow rate of 0.6 mL/min, and detection by refractive index. This method
can separate most sugars, ethanol, xylitol, and acetate. Manose and galactose comprised less than
2% of the total biomass and is counted as xylose because xylose, galactose, and manose co-elute
on the HPX-87H column.

44

The concentrations of ferulic acid and p-coumaric acid in the pretreatment liquor after
AHP pretreatment were determined by HPLC using a Discovery C18 column (126489-04,
Supelco, Bellefonte, PA) operating at 40 oC using a step-gradient of 1% (v/v) acetic acid and
methanol at a flow rate of 0.25 ml/min. The mobile phase started with 100% of the 1% (v/v)
acetic acid in water and 0% methanol for 2min then step-wise increased the methanol
concentration by 2.5% every minute up to 40% and held for 2 min. Then the column was
flushed by increasing to 50% methanol for 3 min before being returned to 100% acetic acid. The
elution times were 12.4 min and 14.2 min for p-coumaric acid and ferulic acid respectively.
Results and Discussion
Hydrogen Peroxide Effects
AHP pretreatment of corn stover and switchgrass was performed using four different
H2O2 loadings, 0%, 6%, 12.5% and 25% (g H 2O2/g biomass) to determine the effect of H 2O2 on
xylan and lignin removal/solubilization, enzymatic digestibility, and hydroxycinnamic acid
release. Table 2 shows the compositions following delignification of glucan, xylan, and lignin
for both types of biomass and their respective glucose yields. AHP pretreatment increased
glucan content by as much as 15% for corn stover and 18% for switchgrass. Lignin content was
reduced by as much as 15% for corn stover and 13% for switchgrass, which corresponds to 65%
and 77% lignin removal respectively. Figure 5A shows the glucose and xylose yields after 7
days of hydrolysis for both corn stover and switchgrass as a function of H2O 2 loading. Glucose
yield increased from 26% to 85% for corn stover over the range of conditions used and 15% to
52% for switchgrass and there is a clear positive correlation between sugar yield and H 2O2
loading. Sugar yields were higher for corn stover than switchgrass for both glucan and xylan,
however, the improvement from the 0% H 2O2 loading condition to the 25% H2O2 loading
45

condition is similar between the two materials. Therefore, under the conditions of this study,
25% H2O2 loading improved glucan digestibility by approximately 35% over alkali alone for
both corn stover and switchgrass. However, corn stover improved almost 60% from untreated
material compared to 40% improvement for switchgrass. Figure 5B shows the same sugar yields
as a function of lignin removal, and again a near linear correlation is observed for both types of
biomass for this range of conditions. However, Li et. al. found a sigmoidal relationship between
lignin content and glucose yield for a range of different grass species (M. Y. Li, et al., 2012).
This particular switchgrass is not particular well suited for AHP pretreatment evidenced by the
relatively low glucose yield, 52%, at the most severe pretreatment condition. However, other
types of switchgrass may perform better. For example, the Cave-In-Rock cultivar, which is used
here, has been shown to exhibit lower in vitro ruminant digestibilities relative to improved
upland cultivars such as Shawnee (Sanderson & Burns, 2010) or to lowland cultivars (Burns,
Godshalk, & Timothy, 2008).
Table 2: Glucan, xylan and lignin contents of untreated and AHP pretreated corn stover and
switchgrass along with glucose yields

Corn Stover
Untreated
Glucan
Xylan
Klason Lignin
Glucose Yield

33.2%± 0.7%

37.5%± 0.1%

37.0%± 1.0%

44.0%± 0.4%

48.5%± 0.4%

21.5%± 0.1%

22.7%± 0.3%

23.5%± 0.4%

23.7%± 0.4%

25.2%± 0.4%

20.5%± 0.6%

19.0%± 0.4%

16.8%± 1.3%

12.9%± 1.6%

11.8%± 1.6%

26.4%± 0.7%

48.2%± 0.1%

51.0%± 0.2%

64.6%± 0.2%

84.7%± 0.9%

Switchgrass
Untreated
Glucan
Xylan
Klason Lignin
Glucose Yield

AHP Delignified (g H2O2 / g biomass)
0 g/g
0.06 g/g
.125 g/g
25 g/g

AHP Delignified (g H2O2 / g biomass)
0 g/g
0.06 g/g
.125 g/g
25 g/g

27.4%± 0.3%

33.7%± 0.1%

36.6%± 0.4%

37.5%± 0.5%

45.0%± 0.5%

20.5%± 0.1%

25.2%± 0.1%

27.0%± 0.1%

28.0%± 0.3%

24.7%± 0.2%

21.9%± 0.3%

19.3%± 0.4%

18.2%± 1.2%

14.3%± 0.1%

8.9%± 0.4%

15.2%± 0.5%

20.1%± 0.1%

29.9%± 1.3%

47.8%± 0.2%

52.2%± 1.2%

46

Figure 5: Glucose and xylose hydrolysis yields as a function of A) hydrogen peroxide loading
and B) lignin removal

Ferulic acid and p-coumaric acid are known to be ester linked to carbohydrates and ester
or ether linked to lignin and they are important for cell wall reticulation. (Iiyama, Lam, & Stone,
1990). Figure 6 shows the release of ferulic and p-coumaric acids as a function of H2O2 loading
during AHP pretreatment for both corn stover and switchgrass. Again, near linear increases in
acid release were observed as a function of hydrogen peroxide loading. Ferulic acid release
expressed as a percentage of the original biomass ranged from 0% to 0.3% for corn stover and
0% to 0.2% for switchgrass, and p-coumaric acid ranged from 0.15% to 1% for corn stover and
0% to 0.7% for switchgrass. Larger amounts of ferulic acid were released with higher H2O2
concentrations and this may be due to cleavage of bonds more resistant to alkali alone or it may
be that as more surface area is exposed larger quantities of easy-to-release ferulates are exposed
(Hartley & Ford, 1989). It may also be that H2O2 oxidation of lignin allows for the release of
ferulic acid that normally would not be fully releasable due to a mechanism of formation where
ferulate radicals cross-couple with lignin radicals (Hatfield, Ralph, & Grabber, 1999).
47

Correlating ferulic acid release with enzymatic digestibility is difficult because of other impacts
on cell wall properties that improve hydrolysis such as lignin and xylan solubilization and lignin
hydrophobicity (Grabber, Hatfield, & Ralph, 2003; Grabber, Ralph, & Hatfield, 1998b).
Howerever, the improvement of digestibility with increased release of phenolics has been known
for some time (Grabber, Ralph, Lapierre, & Barriere, 2004; Hartley & Ford, 1989; Hatfield, et
al., 1999), and ferulate cross-links between lignin are thought to inhibit enzymatic hydrolysis rate
but not necessarily the extent of saccharification (Grabber, Ralph, & Hatfield, 1998a). Ferulic
acid that is ether-linked to lignin is also ester-linked to cell wall carbohydrates (Iiyama & Lam,
2001), therefore, the more difficult-to-remove ferulic acid is an indication of more cross-linking
which corresponds to increased recalcitrance (Jung, Samac, & Sarath, 2012). Uncondensed
lignin containing only beta-O-4 bonds absorbs more flat on the cellulose surface, thus covering
more surface area and occluding cellulose access to enzymes (Besombes & Mazeau, 2005).
Further, Zhang et al. have suggested that p-coumaric acid esterification occurs primarily in the
uncondensed lignin associated with syringyl lignin and corresponds to a decrease in cell wall
digestibility (Zhang et al., 2011). Therefore, these results show that AHP pretreatment can
remove more ferulates and p-coumarates than alkali alone, and this may be a useful method for
quantifying these different types of cross-links.

48

Figure 6: Ferulic and p-coumaric acid release (solubilization) as a function of hydrogen
peroxide loading during AHP delignification

Figure 7A shows the relationship between lignin and xylan removal for corn stover and
switchgrass for all the AHP pretreatment conditions used. Interestingly, there is a linear
correlation, R2=0.915, between xylan and lignin removal when combining data from both types
of biomass instead of having different slopes of linearity between the different biomasses, which
was common for other correlations between properties investigated. Figure 7B shows the
correlation between lignin removal and ferulic acid release from both corn stover and
switchgrass, and there is a linear correlation between these variables for both materials, however,
the slopes are different. These linear relationshipshave been observed for other AHP pretreated
grasses and is thought to be evidence that they are removed concurrently during pretreatment (M.
Y. Li, et al., 2012). This is reasonable since ferulic acid and xylan are linked to lignin, it would

49

be expected that their removal would be concurrent. It is interesting that xylan and ferulic acid
removal trends have different slopes for corn stover and switchgrass which would indicate that
there is a difference in the amount of cross-linking between these two components, corn stover
having more xylan-lignin cross-linking than switchgrass.

Figure 7: Correlation between A) xylan and lignin removal for corn stover and switchgrass and
between B) lignin and ferulic acid removal

Figure 8 shows the fractions of monomeric xylose and solubilized oligomeric xylan
released following AHP pretreatment and enzymatic hydrolysis of corn stover and switchgrass as
a function of H2O2 loading. Oligomeric xylan comprised from 15% to 35% of the solubilized
xylan for corn stover and 2% to 23% for switchgrass depending on the H2O2 loading. The
presence of xylo-oligomers in the hydrolyzate is known to be strongly inhibitory to cellulose
hydrolysis (Brienzo, Carvalho, & Milagres, 2010; Qing & Wyman, 2011). Unhydrolyzed xylan
following AHP pretreatment and enzymatic hydrolysis may be due to substitutions such as
ferulic acids, arabinans, and glucuronic acids on the xylan backbone that limit enzyme hydrolysis

50

beyond chains of a certain length by physically occluding the enzyme. Arabinose substitution on
xylan has been shown to negatively impact digestibility (F. Li et al., 2012).

Figure 8: Fraction of solubilized xylan following AHP pretreatment and enzymatic hydrolysis
that is monomeric and oligomeric as a function of H 2O2 loading for A) corn stover and B)
switchgrass

Figure 9 shows the kinetics of hydrogen peroxide consumption during AHP
delignification for two different hydrogen peroxide loading conditions, 0.15 and 0.25 g/g. There
is an initial drop in detectable H2O2 at time 0, likely due to the deprotonation of some fraction of
the hydrogen peroxide at the relatively high pH. Much of the H 2O2 is consumed, 40% and 60%,
for the 15% and 25% g/g conditions respectively, within the first 3 hours, and the remaining
being consumed within 24 hours. Gould et. al. also showed that ~20% of the oxygen in H 2O2 is
incorporated into kenaf for the AHP conditions they used at a solids concentration of 2% (w/v),
and that improvement of incorporation, to ~40%, could be achieved simply by performing
pretreatment at a higher solids concentration, 6% w/v, and further, that the initial rate of O 2
evolution was increased at this higher concentration (Gould, 1985). Observations in our lab have
shown up to 40% oxygen incorporation in corn stover (data not shown).
51

Figure 9: Hydrogen peroxide utilization with time for 0.12 and 0.25 g/g hydrogen peroxide
loading

Figure 10 shows the sodium hydroxide consumption during AHP pretreatment of corn
stover (A) and switchgrass (B) with and without hydrogen peroxide at 2% and 8% solids
concentrations. For both materials saturation is approached where consumption no longer
increases as alkali loading is increased. This is indicative of the limit in acid release from
biomass. Also, when the solids loading is increased, the saturation level of consumed alkali also
increases indicating that higher solids loading perhaps improves the effectiveness of
pretreatment, although the reason for this is not clear. It could simply be that at higher solids
concentrations with a constant alkali loading the liquid phase alkali concentration is higher and
therefore the pretreatment is more effective at removing acid groups from the biomass.

52

Figure 10: Sodium hydroxide consumption during AHP delignification for corn stover (A) and
switchgrass (B) for 2% and 8% solids concentration

High Solids Scale-up
Much of the AHP pretreatment work that has been done in the literature was performed at
the bench-scale on the order of several g of biomass pretreated at a time. Banerjee et. al. were
among the first to report results of AHP pretreatment at a larger scale, 1 kg of corn stover, and
53

pretreatment was integrated with enzymatic hydrolysis and fermentation (Banerjee et al., 2012).
A glucose yield of 75% was achieved in their work for an H 2O2 loading of 0.125 g/g biomass,
and a solids concentration starting at 13% (w/w), and further, no washing was performed after
pretreatment indicating that the impact of inhibitors formed during pretreatment was relatively
minimal. In continuation of that work, and to determine the effects of solids concentration at
the1 kg scale on AHP pretreatment effectiveness, corn stover was pretreated as described above
using an industrial bowl mixer at solids concentrations >15% w/v and enzymatically hydrolyzed
using the same conditions for 48 h and then compared based on sugar yield. Figure 11 shows the
48 h yields of glucose (A) and xylose (B) for the different pretreatment times as a function of
solids concentration. For 3 h of pretreatment, glucose yield increased with increasing solids
concentration up to 45% then it decreased at 55%, and this pattern is less pronounced for 9 h and
for 24 h of pretreatment. ANOVA, and Tukey’s test analysis of the data indicate that there are
statistical differences between yield values with some acceptions for the longer pretreatment
times, where some values cannot be considered statistically different based on 95% confidence
(additional statistical analysis of the data set is shown in the appendix of this chapter.) However,
it can be said that higher solids concnetraitons reduces pretreatment times required to reach
maximal levels of digestibility. It should be noted that the temperature of the pretreatment
increased with solids concentration from 25 oC for the 15% condition to 85 o C for the 55%
condition, likely due to the exothermic degradation of H 2O2. This is likely a consequence of
higher liquid phase concentration of H2O2 at higher solids concentrations and the increase in heat
dissipation times at the larger scale. Saha et. al. examined the effect of temperature and
pretreatment time on pretreatment effectiveness based on sugars released after enzyme
hydrolysis and found that at 25 oC pretreatment improvement stopped after 6 h, but at 35 oC there

54

was not much improvement after 3 h of pretreatment for wheat straw at 9% solids (w/v) (Saha &
Cotta, 2006). The same trend is observed for xylose yield as glucose shown in Figure 11B,
however, the maximum achieved yield is at 25% solids and not 45% as the case for glucose
yield. This may be due to an increased susceptibility of xylan to oxidative degradation in the
liquid phase compared to glucan due to xylan solubilization during pretreatment and the presence
of higher liquid concentrations of H2O2 at higher solids concentrations. The susceptibility of
solubilized glucan may also be the reason for decreased yields for the 55% solids condition.
Porro et. al. have shown that at a high enough NaOH concentration, around 8%, Na-cellulose
starts to become soluble (Porro, Bedue, Chanzy, & Heux, 2007). Therefore, it is possible that
any solubilized cellulose may be oxidatively degraded especially at the higher solids
concentration conditions. However, the oxidative degradation of sugar was not directly
determined for these conditions and is simply a hypothesis. It should also be noted that by
running hydrolysis for longer times, conversion values may be improved by as much as 5-10%;
the 2 day hydrolysis times were chosen for simplicity as a basis for comparison.

Figure 11: A) glucose yield and B) xylose yield of AHP pretreated corn stover as a function of
solids loading and pretreatment time for the large scale pretreatment

55

Quantification of ferulic and p-coumaric acids in the high solids pretreatment samples
was not performed during the scale-up experiments describe above. Therefore, small-scale high
solids pretreatments of corn stover were performed at a low and high temperature mimicking the
least and most severe temperatures experienced during the scale-up experiments. Figure 12
shows the concentrations of p-coumaric and ferulic acids as a function of pretreatment solids
concentration for two different pretreatment temperatures, 25 oC and 85 oC. As the solids
loading increases the liquid-phase concentrations of both acids saturates (A). However, this does
not represent the maximum amount of these compounds in the biomass material as indicated in
Figure 12B; as the solids concentration is increased, the amount of acids released per mass of
biomass decreases indicating that these compounds are either degraded further or the solubility
limit has been reached and they are simply precipitating out of solution. Which of these is
responsible has not been determined. However, the higher pretreatment temperature increases
both acid concentrations suggesting the increase is due to increased solubility at the higher
temperature.

56

Figure 12: Cinnamic acid released expressed as A) g/L liquid phase concentration and B) g/g
corn stover concentration as a function of solids concentration and temperature during small
scale AHP pretreatment of corn stover

Conclusions
AHP pretreatment of corn stover and switchgrass improved enzymatic glucose and
xylose yield ~30% over mild alkali alone for the conditions used in this work. Corn stover
glucose yields reached as high as 85% while switchgrass was only 52%. These results indicate
that the switchgrass used, Cave-in-Rock, is not well suited for AHP pretreatment, since typically
sugar yields of 80% or higher are desired. In addition to H 2O2 loading significantly impacting
enzymatic digestibility, there was also noted impacts on lignin and xylan removal, and ferulic
and p-coumaric acid release. Lignin removal ranged from 20-65% and 25-77% for corn stover
and switchgrass respectively over the range of conditions used. And xylan removal ranged from
9-30% and 4-37% for corn stover and switchgrass respectively. Interestingly, lignin and xylan
removal were compellingly linearly correlated suggesting they are removed concurrently, which
has been suggested elsewhere (M. Y. Li, et al., 2012). Ferulic and p-coumaric acids were
released in increasing amounts with increasing H 2O 2 loadings suggesting that alkali alone is not

57

sufficient to remove all of these compounds from biomass. Oligomeric xylan accounted for a
notable amount of solubilized xylan during AHP pretreatment, as high as 35% for corn stover
and 23% for switchgrass, which is important because xylo-oligomers are known to be inhibitive
to cellulases. AHP pretreatment of corn stover was performed at the 1 kg scale at solids
concentrations higher than have been reported before, up to 55% w/v, with positive results.
Glucose yields near 70% could be achieved in just two days of hydrolysis, and higher yields
could be achieved for longer hydrolysis times. It was found that the temperature of slurries
increased significantly with solids concentration and that sugar degradation, particularly xylan,
may be a consequence of performing pretreatment at these high solids conditions. Ferulic and pcoumaric acid concentrations increase as well with increasing solids concentrations, however,
some of the acid may precipitate out of solution as the solubility limit is reached for these
compounds. These results demonstrate that AHP pretreatment may be well suited for some
grasses, but perhaps not all, and that performing pretreatment at higher solids concentrations is
possible with good results compared to lower solids concentrations at small scale. However,
H2O2 is expensive and for this process to become more feasible, peroxide addition will need to be
reduced, which could be achieved if it can be used at higher efficiency.
Acknowledgments
I would like to thank Alex Smith for performing the work on NaOH and H2 O2
consumption presented in this chapter.

58

APPENDIX

59

Appendix: Statistical Analysis – Scale-up AHP pretreatment of corn stover
Statistical analysis includes the response variables glucose and xylose yield following
enzymatic hydrolysis and the factors solids concentration, pretreatment time and hydrolysis time
with 5, 5, and 6 levels respectively. Table 3 shows the factors and level values. ANOVA
analysis on all the data was performed to determine differences in values of the response values.
Table 4 below shows the ANOVA tables for glucose and xylose yield. The p values are all 0.000
indicating that the null hypothesis (all mean values are the same) should be rejected, which
indicates that mean values are not the same within each factor. This is expected since each factor
has been shown to impact the response variables. Therefore, an interaction plot was generated
for each response variable, and Figures 13 and 14 shows these interaction plots for glucose and
xylose yield for all three factors and their respective levels.
The interaction between pretreatment time and hydrolysis time for glucose yield in Figure
13 (bottom right graph) shows that increasing pretreatment time increases the glucose yield at all
six different hydrolysis times. The same result is observed for xylose yield in Figure 14 where
the increase is more evident. The interaction between solids concentration and hydrolysis time
(upper right graph) shows that increasing solids loading increases yield for some of the solids
concentrations. In particular the higher solids loadings at later hydrolysis times produ ced the
highest glucose yields, however, 25% and 35% solids concentration produced the highest xylose
yields across all hydrolysis times. This indicates that increasing solids concentration improves
glucose yield but decreases xylose yield likely due to oxidative degradation. The solids
concentration and pretreatment time interaction (leftmost graph) indicates again that solids
concentration improves glucose yield but too high a concentration will decrease xylose yield.

60

Table 3: list of factors and levels for design of experiments statistical analysis

Factors

Levels
15%, 25%, 35%, 45%, 55%
3, 6, 9, 12, 24
2, 4, 6, 8, 24, 48

Solids Concentration (% w/v)
Pretreatment Time (h)
Hydrolysis Time (h)

Table 4: ANOVA tables for glucose (top) and xylose (bottom) yield including all three factors
Anova Table for Glucose Yield
Source
DF
Seq SS
%Solids
4
0.10277
Pretreat
4
0.06789
Hydrol
5
4.34762
Error
286
0.21454
Total
299
4.73282

Adj SS
0.10277
0.06789
4.34762
0.21454

Adj MS
0.02567
0.01697
0.86952
0.00075

F
34.25
22.63
1159.14

P
0.000
0.000
0.000

Anova Table for Xylose Yield
Source
DF
Seq SS
%Solids
4
0.35698
Pretreat
4
0.16829
Hydrol
5
0.9798
Error
286
0.13406
Total
299
1.63914

Adj SS
0.35698
0.16829
0.9798
0.13406

Adj MS
0.08925
0.04209
0.19596
0.00049

F
190.39
89.76
418.04

P
0.000
0.000
0.000

61

Figure 13: Interaction plot for glucose yield

Figure 14: Interaction plot for xylose yield

62

The Tukey Method for pairwise comparisons within each factor at each factor level was
used to determine which conditions yielded statistically significant differences in glucose and
xylose yield values. Table 5 shows the summary results of the Tukey Test assigning alphabetic
labels to levels that have statistically different means. These results indicate that only 25% and
35% solids have similar means for glucose and xylose yields, and 45% and 55% solids for xylose
yields are statistically similar. Glucose and xylose yields are statistically different for the short
pretreatment times of 3 and 6 h and then there is some overlap for the later times of 9, 12 and 24
h. Hydrolysis time, as expected, yielded statistically different means for all times investigated.
However, these results include all three factors and levels, and may give different results if only
one hydrolysis time was used. To test this, a pairwise t-test was performed on only the 48 h
hydrolysis time for glucose yield. Table 6 shows the resulting groupings, and it is apparent that
overlapping groupings increases with pretreatment time, which indicates that there are less
statistical differences between glucose yield values at different solids loading as pretreatment
time increases. This is indicative of the fact that pretreatment has reached maximal effectiveness
at longer pretreatment times.
Table 5: Summary of groupings using Tukey’s Test for all three factors and their levels based on
95% confidence interval

Glucose Xylose
Grouping
Grouping
%Solids
0.15
C
B
0.25
A
A
0.35
A
A
0.45
B
C
0.55
A
C

Glucose Xylose
Grouping
Grouping
Pretreat
3
D
D
6
C
C
9
BC
B
12
AB
B
24
A
A

63

Glucose Xylose
Hydrol Grouping Grouping
2
F
E
4
E
D
6
D
C
8
C
C
24
B
B
48
A
A

Table 6: Summary of groupings for glucose yield using a pairwise t-test for 48 h hydrolysis time
and select pretreatment times: 3, 9, 24 hours
Solids
15
25
35
45
55

3 hours
A
B
C
D
C

9 hours
A
B
BC
BD
D

24 hours
A
AB
ABC
BD
CD

64

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65

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69

Chapter 2 – Bench scale combined diffuser type extraction and alkali pretreatment of
sweet sorghum sugars and bagasse
Introduction
With the projected increase in demand for liquid fuels, predominantly used for
transportation, the production of ethanol has increased significantly worldwide in an effort to
meet demands while decreasing reliance on petroleum (Babcock, 2012). Most ethanol produced
utilizes a sugar platform wherein carbohydrates are extracted from sugar or starch rich crops,
such as sugar cane and corn, which are then fermented to make ethanol. Another potentially
valuable feedstock, particularly in North America where sugar cane does not grow very well, is
sorghum, specifically sweet sorghums which have huge breeding potential and, like sugar cane,
have high sugar contents that are readily fermentable to ethanol (G. Eggleston, Cole, &
Andrzejewski, 2013). Furthermore, sweet sorghums require a third less water and less fertilizer
than corn making them even more attractive as a bioenergy feedstock (G. Eggleston, et al.,
2013). Life-cycle analysis has shown that sorghum-based ethanol can have a significant
reduction on green house gass (GHG) emissions and reduced fossil energy consumption, as
much as 72% and 84% respectively compared to petroleum (Cai, Dunn, Wang, Han, & Wang,
2013). The downside is that sweet sorghums can lose their sucrose content quickly, often within
hours of being cut (G. Eggleston, et al., 2013) which would require that stalks be processed on
site and sugars be stabilized or perhaps fermented at a local location before being transported to a
centralized biorefinery for further processing. In addition to the non-structural carbohydrates
such as glucose, sucrose, fructose and starch which comprise around 40% or more of the total
carbohydrate content of sweet sorghums and are relatively easy to extract, as much as 50-60% of
the dry plant material is structural carbohydrates such as cellulose and hemicelluloses (Rooney,
70

Blumenthal, Bean, & Mullet, 2007). However, there is a lack of processing technologies that are
geared toward the utilization of both the non-structural and structural carbohydrates that would
make using this crop commercially attractive (G. Eggleston, et al., 2013).
Sweet sorghum can be processed similar to sugar cane (Webster, Hoare, Sutherland, &
Keating, 2004; Woods, 2000). In fact, a study on sweet sorghum harvesting, milling and
extraction using a sugar cane mill was done in Australia and it was found that sweet sorghum can
be harvested to the same bulk density as cane and that cane mills can achieve Brix extraction
upward of 75%, which is a typical maximum purity for sorghum (G. Eggleston, et al., 2013),
compared to 88% for sugar cane (Webster, et al., 2004). Therefore, existing technologies for
sugar cane can be used, albeit modified, for processing of sweet sorghum to obtain relatively
high Brix extractions, however; bagasse carbohydrates are left unused or are burned and used for
plant process energy. Cane milling and sugar extraction is typically done in one of a few
different ways: tandem roller-mills, screw press extraction, or diffuser extraction. Tandem roller
mills squeeze juice from the stalk, however, at low effeciencies (~85%) (Gnansounou, Dauriat,
& Wyman, 2005). Screw press extraction can squeeze juice from chipped or ground stalks but
efficiency is also low, ~65%, and the feed rate is too slow for large-scale use (Weitzel, Cundiff,
& Vaughan, 1989). Diffuser extraction of shredded stalks is the most common technique and
has efficiencies as high as 98% for cane, although impurity content is higher too (Rein, 1995).
Disadvantages of diffusion are higher levels of sand in the baggase, and longer equipment start
up and shut down procedures due to larger cane holding capacity within the equipment.
However, the capital cost ratios of 1:1.5 or higher have been estimated for diffusion to milling
plant style sugar extraction (Rein, 1995). Diffuser extraction technology could also be used to
facilitate utilization of carbohydrate polymers such as starch or cellulose. It may be possible to
71

add amylases during the diffusion extraction to facilitate starch hydrolysis to fermentable sugars
(G. Eggleston, et al., 2013), or alkali to simultaneously perform extraction and pretreatment.
This would yield extraction juice with higher impurity content, but if the goal is to make a
fermentable sugar stream for biofuels, that may not be an issue.
Cane sugar streams typically require some stabilization, clarification and evaporation to
syrup if the juice is to be stored or transported to a separate facility for fermentation than the
milling/extraction site (Eggleston, et al., 2013). The stability of sweet sorghum juices and syrups
has not been investigated as extensively as those from cane, and there is a lot of opportunity for
research in this area especially for utilization to make biofuels where the requirements for
stability may not be as stringent. Furthermore, if lignocellulosic sugars are to be used in addition
to the extractable ones, there are other issues that would need to be addressed. Challenges in
performing fermentation of lignocelluosic hydrolyzates include hydrolyzate toxicity, the desire
to co-ferment glucose and xylose, the desire to perform pretreatment and hydrolysis at high
solids concentrations to yield high ethanol titers, and the need the achieve high overall yield cost
effectively. Hydrolyzate toxicity to fermentation depends on the pretreatment technology
employed and even the feedstock used where inhibitors include organic acids, phenolics, furans
and inorganics (Luo, Brink, & Blanch, 2002; Palmqvist & Hahn-Hagerdal, 2000). For example,
high temperature acid pretreatments can degrade xylose to furfural and formic acid, while
mannose, galactose and glucose are known to degrade to hydroxymethylfurfural and levulinic
acid (Palmqvist & Hahn-Hagerdal, 2000). Other potential inhibitors include the
hydroxycinnamic acids ferulic and p-coumaric acid. Ferulic acid is ester-linked between xylan
and lignin and p-coumaric acid is acetylated to the side chains of lignin (Harris & Stone, 2008).
Alkali and acid pretreatments are known to cleave these bonds releasing the acids (shown in
72

chapter 1). Combining extracted juice or syrup from sweet sorghum with lignocellulosic
hydrolyzate has the advantage of diluting inhibitor concentrations in the hydrolyzate with the
juice, and as long as sugar concentrations are comparable in the two streams, high titers of
ethanol would still be possible.
In this chapter, a baseline of liquid hot water (LHW) and alkali pretreatment impacts on
sorghum bagasse will be established based on composition changes and enzymatic digestibility
improvement. Then a combined sugar extraction and alkali pretreatment technique will be
performed on sweet sorghum integrated with enzyme hydrolysis and fermentation as a proof of
concept for a potential process scheme utilizing both the extractable and structural carbohydrates
in sorghum for biofuel production.
Methods and Materials
Biomass
A sweet sorghum bioenergy hybrid (TX08001) was obtained from John Gill at Texas
A&M, bred to have high biomass yield and not necessarily high extractable sugar content.
Sorghum was milled to pass a 5 mm screen and dried to a moisture content of ~8%. Water
extractives and sugar content as well as structural carbohydrate and lignin content of the
sorghum and bagasse were determined using the NREL analytical protocols NREL/TP-51042619 and NREL/TP-510-42618 respectively, the later performed with modification as described
previously (Li et al., 2012). Bagasse was prepared by washing 15 g of sorghum at a time with
600 mL of 80 oC water using a Buchner funnel with a 200 mesh porous base. The bagasse was
then allowed to air dry before further use in composition analysis and batch pretreatment and
hydrolysis experiments.

73

LHW and Alkali Pretreatment
Air dried bagasse was pretreated using two conditions of LHW and alkali pretreatments.
LHW pretreatment was performed at 15% solids (w/v) by adding 6 g of bagasse and 40 mL of
water into an Ace Glass pressure tube (8648-162) and sealed with the thred-cap. Tubes were
then placed in an autoclave for 1 h at 120 oC for the first condition, and a 5 L M/K Systems
digester (M/K Systems, Inc., Peabody, MA) was used to heat tubes to 160 oC for 1 h for the
second condition. The pretreated bagasse was then diluted to 10% solids (w/v) and placed in
flasks in preparation for hydrolysis. Alkali pretreatment was performed in shake flasks at 15%
solids (w/v) and NaOH loadings of 0.1 and 0.06 g NaOH/g bagasse. The flasks were incubated
at 80 oC for 1 h in a water bath, and then diluted to 10% (w/w) and the pH was adjusted to 5.5
using concentrated sulfuric acid in preparation for enzymatic hydrolysis.
Sugar Extraction and Alkali Pretreatment
Sugar extraction was performed using a series of five Buchner funnels and filter flasks;
the Buchner funnels had a 3.8 cm diameter, 200 mesh stainless steel porous base. 5 g of fresh
sorghum was introduced at one end of the series of funnels, designated stage #5, and moved
sequentially to the right during the process, and 25 mL of fresh water was introduced at the other
end, designated stage #1 and moved sequentially to the left during the process (see Figure 17 for
schematic of process, and a picture of the apparatus and process is shown in the appendix to this
chapter as Figure 21). This work will be investigating scheme 2 illustrated in Figure 17.
Therefore, at stage#2 0.51 mL of 5 M NaOH solution is added to the extraction water from stage
#1 and the slurry of biomass, water and NaOH was incubated at 80 oC for 60 min in a water bath
before continuing to the next stage. Extraction juice after filtering at stage #5 was filter sterilized
using a 0.22 µm stericup and stored at 4 o C until fermentation was performed. The extracted
74

sugar at each stage was determined using the HPLC protocol described in the NREL/TP-51042618 method. The bagasse exiting stage #1 was immediately prepared for enzymatic
hydrolysis.
Enzymatic Hydrolysis
For the batch LHW and alkali pretreated bagasse samples, pretreated material was diluted
to 10% (w/w) solids and the pH adjusted to 5.5 as needed. Then hydrolysis samples were
prepared as follow: citrate buffer, tetracycline and cyclohexamide were added to 50 mM and 10
mg/L respectively, and CTEC2 and HTEC2 were added in a protein ratio of 2:1 for an enzyme
loading of 15 mg enzyme/g glucan. Samples were then incubated at 50 oC for 7 days. For the
combined sugar extraction and pretreatment process, the bagasse leaving stage #1 was diluted to
~18% solids (w/w) and the pH adjusted to 5.5 using concentrated sulfuric acid. Citrate buffer,
antibiotics and enzymes were then added and samples were incubated as described above;
hydrolysate was then filter sterilized using a 0.22 µm stericup and stored at 4 oC until
fermentation was performed. Sugar concentrations in the hydrolysates were determined by
HPLC using the method described in the NREL / TP 510-42618 protocol and converted to
glucose and xylose yields based on the glucan and xylan contents of the untreated bagasse.
Fermentation
Fermentation of a combination of the extracted juice and hydrolysate from the scheme 2
extraction and alkali pretreatment process was performed using S. cerevisiae GLBRC Y73 yeast
strain. Juice and hydrolysate were mixed in a 60:40 v/v ratio (which is same ratio of liquid
exiting both stages of the process) and yeast nitrogen base (YNB) and urea were added to the
mixture to yield concentrations of 1.67 and 2.27 g/L respectively to serve as nutrients. The yeast
seed culture was prepared by inoculating 50 mL of YNB medium (10 g/L yeast extract, 20 g/L
75

peptone and glucose) with the glycerol stock of Y73, and then incubating for 24 h at 30 oC with
150 rpm shaking. After 24 h, 10 mL of culture was transferred aseptically to 60 mL of the
juice/hydrolysate mixture in a shake flask, in duplicate. The flasks were covered with
fermentation locks, sparged with nitrogen, and incubated at 30 oC with 150 rpm shaking for 7
days. Samples were collected every 24 hours to determine OD600 and sugar and ethanol
concentrations using HPLC as described above.
Results and Discussion
Figure 15 shows the sugar composition of the sweet sorghum TX08001, including the
water extractable and the structural carbohydrates. This sorghum is a bioenergy hybrid designed
to have high biomass yield and not extractable sugar, therefore, only 44% extractives on a dry
weight basis is reasonable. The goal if this work is to utilize as much of the structural glucan and
xylan as possible for fermentation to ethanol along with the extractable sucrose, glucose and
fructose, which together accounts for 57% of the dry material. To determine a baseline of how
digestible the bagasse is to enzymes, LHW and alkali pretreatment were performed on the
bagasse after washing out the extractives as described above. Table 7 shows the impact of the
two LHW and alkali pretreatment conditions on solids removal during pretreatment and the
composition of glucan, xylan and lignin after pretreatment. Glucan contents are similar for the
alkali and the higher temperature LHW pretreatment conditions. Also, lignin content is
noticeably higher for LHW than alkali, which is expected since alkali pretreatment is known to
solubilize some lignin, while LHW does not, but does redistribute it on the biomass surface.
Figure 16 shows the glucan and xylan yields of enzymatic hydrolysis of the four pretreatments
and untreated bagasse. Digestibility improvement from the untreated condition is largest for the
more severe alkali condition for glucan (87%) and the more severe LHW condition for xylan
76

(40%). Regardless of pretreatment type or condition, glucan digestibilities were improved to
near 70% or higher over the 50% yield of untreated bagasse, and this is without attempting to
optimize the pretreatment conditions or the enzyme mixture for hydrolysis. Therefore, this
material may still have much potential for improvement.

Extractives 44%

56%
Bagasse

15%

Other

10%
7%

Sucrose
Glucose

12%

Fructose

11%

Glucan

17%

Xylan

9%

Lignin

19%

Other

Fermentable
Sugars – 57%

Figure 15: Composition of carbohydrates, both soluble and structural, in the sweet sorghum

Table 7: Impact of LHW and alkali pretreatments on solubilized material during pretreatment
(removed) and glucan, xylan and lignin content of residual solids after pretreatment

77

100%

Alkali 0.1 g/g NaOH
Alkali 0.06 g/g NaOH

80%

Yield

LHW 160 C for 1 h
LHW 120 C for 1 h

60%

Control

40%
20%

0%

Glucose Yield

Xylose Yield

Figure 16: Glucose and xylose yields for LHW pretreated, alkali pretreated and untreated
bagasse
Figure 17 illustrates a few different process integration schemes for generating sugar
streams of different compositions depending on the source. Scheme 1 is similar to the batch
process performed above where sugar extraction is done, typically a diffuser type process,
followed by separate pretreatment on the bagasse, which depending on the pretreatment
technology, could generate a stream of solubilized xylan or lignin for example with alkali
pretreatment. The pretreated solids could then be hydrolyzed to make a stream of glucose and
xylose from structural carbohydrates leaving a solid residue relatively high in lignin content and
unhydrolyzed carbohydrates that could be burned for energy. In scheme 2, a combined sugar
extraction with alkali pretreatment would be done by adding alkali to the wash water. Combined
alkali extraction and pretreatment may promote a more stable juice that is not susceptible to
infection from Leuconostoc mesenteroides which is the major contributor to microbial
deterioration of sugar crops such as sugar cane and sugar beets (Eggleston, Monge, & Ogier,
2003). The juice stream would not be as clean, meaning it would contain Na+, lignin and maybe
some xylan, which would not be desirable in conventional sugar extraction where the sugar is
78

used to make high purity products like crystallized sugar or syrup, but for ethanol fermentation
that would not be an issue, and lower purity juices may be sufficient. Other types of
pretreatment could be used in this scheme including enzyme addition, hemicellulases or other
accessory enzymes, or amylases to hydrolyze starch could be added during washing to make the
bagasse more amenable to the cellulolytic hydrolysis process following. In scheme 3,
pretreatment is combined with extraction again, but the pretreatment liquid is separated before
reaching the less extracted sorghum upstream thus providing a separate sugar stream that leaves
the juice free of impurities the pretreatment would generate.

79

Imbibation
Water

Scheme 1

Bagasse

Sorghum
5

4

3

2

1

Soluble
Xylan, Lignin

Glc, Fru, Suc

Pretreated
Bagasse

Sorghum
4

3

Glc, Fru, Suc
Xylan, Lignin

2

1

Glucose
Xylose

Enzyme
Hydrolysis

Imbibation
Water

Imbibation
Water

Pretreated
Bagasse

Sorghum
4

4

Hydrolysis
Residue

Glucose
Xylose

NaOH

Scheme 3

5

Enzyme
Hydrolysis

Hydrolysis
Residue

Imbibation
Water

Scheme 2

5

Pretreatment
(LHW or alkali)

Pretreated
Bagasse

3

2

1

Glc, Fru, Suc

Enzyme
Hydrolysis

Hydrolysis
Residue

Glucose
Xylose

NaOH

Xylan, Lignin

Figure 17: potential process schemes for integrating sugar extraction and bagasse pretreatment
and hydrolysis of sorghum to generate various sugar streams that can be utilized for fuel
production, or other sugar-based products

As a proof of concept, scheme 2 using 0.06 g NaOH/g bagasse alkali pretreatment during
sugar extraction followed by enzymatic hydrolysis and fermentation of the combined juice and
hydrolysate was performed as described above. Figure 18 shows a non-exhaustive mass balance
flow diagram of the whole process indicating sugar concentrations in the juice and hydrolysate
and the amounts of water and solid residue exiting each operation. The juice and hydrolysate
80

were combined at a liquid ratio of 60:40, which does dilute the sucrose and fructose
concentrations in the juice and the glucose and xylose concentrations in the hydrolysate where
they are higher. Figure 19 shows the sugar profile for the extraction stages and the pH value at
each stage. The process does reach near 100% washing efficiency as the sugar concentration at
stage 5 is close to 0. The sugar concentrations at stage #5 are maximal for the solids
concentration used for washing. However, the particle size of sorghum during extraction is
important and does impact the yield of sugar extraction (Jia, Chawhuaymak, Riley, Zimmt, &
Ogden, 2013).Water extraction which recycles the water, has been shown to remove sugars more
efficiently than the press method (Jia, et al., 2013). The leaves and pith can absorb juice lowering
the extraction yield (G. Eggleston, et al., 2013; Whitfield, Chinn, & Veal, 2012). It is also
interesting to note that the pH drops from 12.5 at the start of the pretreatment at stage #2 to 5.2 at
stage #5. This is fortuitous because the desired pH for fermentation is 5.5, which means no pH
adjustment of the juice is required after extraction. Figure 20A shows the fermentation results.
All of the sucrose, glucose and fructose are used in the first 16 hours, indicating that inhibition of
the juice/hydrolysate is minimal, and ~80% of the xylose was utilized after 7 days. An ethanol
concentration of 21 g/L was obtained which gave a yield of 85% which is comparable to other
results using this same Y73 strain (Liu et al., 2014). Achieving higher sugar concentrations
during extraction was not possible using the apparatus setup developed here due to limitations in
dewatering. Therefore, to determine how fermentable higher concentratrions of the mixed sugar
streams may be, rotary evaporation was used to concentrate the sugar mixture, and then
fermentation was performed as before. The results are shown in Figure 20B. An approximate 6fold increase in sugar concentration was achieved by rotary evaporation to a total sugar
concentration of 332 g/L. Yeast growth was notably slower than the more dilute condition where

81

3 days was required before OD 600 absorbance reached saturation. Sugar consumption was also
significantly slowed where approximately 15-20 g/L of sucrose, fructose and glucose were still
remaining after 5 days of fermentation, compared to complete consumption of these sugars in
less than 18 hours for the more dilute condition. However, an ethanol concentration of 80 g/L
was achieved representing an ethanol yield of 46%. Acetic and lactic acid are known to be
inhibitive to fermentation, and acetic acid more so than lactic acid (Graves, Narendranath,
Dawson, & Power, 2006), however, acetic acid was not detected in the concentration sugar
mixture, likely due to it evaporating during concentrating with rotary evaporation.

0.1 g NaOH

5 g Sorghum
25 mL Water

Extraction/
Pretreatment

3 mL Water

~15 mL Water
~2.25 g Bagasse

Juice
15 mL
20.7 g/L Sucrose
19.4 g/L Glucose
18.9 g/L Fructose

Enzymatic
Hydrolysis

Solid Residue
~3 mL Water

Hydrolysate
10 mL
70% Glucose Yield
18% Xylose Yield
1.3 g/L Sucrose
26.6 g/L Glucose
10.4 g/L Xylose
1.4 g/L Fructose

Fermentation

21 g/L Ethanol
~85% yield

Figure 18: selected mass balance and sugar concentrations in juice and hydrolyzate for the
combined sugar extraction/pretreatment process integrated with enzyme hydrolysis and
fermentation

82

13

12

60

11

50

10
9

40

Fructose

30

Glucose

7

20

Sucrose

6

10

pH

5

8

pH

Sugar Concentration (g/L)

70

4

0

3

5

4

3
Stage #

2

1

Figure 19: sugar profile during extraction for each stage and pH change during the process

83

Concentration (g/L) and OD600

25

A
20
15

OD600

Sucrose

Glucose
Fructose

Xylose
Ethanol

10
5
0

0

160

25

50

B

120

150

Sucrose

Glucose

Xylose
Ethanol

Fructose
OD600

175

8

6

100
80

4

60
40

OD 600

Concentration (g/L)

140

75
100
125
Time (Hours)

2

20
0

0

0

25

50

75
100
125
Time (Hours)

150

175

Figure 20: sugar utilization during fermentation of juice/hydrolysate combination using S.
cerevisiae GLBRC Y73 strain on A) combined juice and hydrolyzate and B) 6-fold increase in
concentration by rotary evaporation

84

Conclusions
The digestibility of a sweet sorghum bagasse was determined for a select set of
conditions using LHW and alkali pretreatment. Glucose yield improvements ranged from 15%
to 35% depending on the pretreatment and condition compared to untreated bagasse. A
maximum yield of 87% was achieved for alkali pretreatment using an NaOH loading of 0.1 g
NaOH/g bagasse. Xylose yields, however, were considerably lower, ranging from 20% to 40%
for the conditions used. Yield improvement from those observed here could be achieved with
pretreatment condition and enzyme optimization specific to this bagasse material. A combined
sugar extraction and alkali pretreatment integrated with enzyme hydrolysis and fermentation of
the sweet sorghum was also investigated to demonstrate that mixed sugar streams that would
normally be considered impure for sugar product utilization could be used for bioethanol
production. The combined extraction/pretreatment process produce juice with near 100%
extraction efficiency that required no pH adjusted prior to fermentation, and the subsequent
hydrolysis of the bagasse reach sugar yields comparable to the batch processes, 70% glucose
yield and 18% xylose yield. Combining the juice stream with the hydrolyzate stream provided a
fermentable mixed sugar solution that achieved an ethanol concentration of 21 g/L at an ethanol
yield of 85%, and 80 g/L at a yield of 46% for a concentrated mixed sugar solution using rotary
evaporation. This demonstrates the potential of combining sugar extraction with pretreatment of
bagasse where the end goal is a fermentable sugar stream and not necessarily a pure one.

85

APPENDIX

86

Appendix: Description of combined extraction/pretreatment setup
Figure 21 below shows the apparatus setup for performing the sugar extraction of
sorghum and alkali pretreatment of the resulting bagasse. Fresh sorghum is introduced at stage
#5 where it is washed with the liquid from stage #4 before the solids remaining are moved to the
right to stage #4. The juice that is passed thru the fresh bagasse is then collected in the beaker to
the left. Fresh water is introduced at stage #1 washing the bagasse from stage #2. The bagasse
after being washed at stage #1 is then collected in the beaker to the right. At stage #2, the liquid
from stage #1 is mixed with 0.51 mL of 5 M NaOH and slurried with the begasse coming from
stage #3 and incubated for 1 h at 80 oC before being filtered thru the funnel at stage #2.
Therefore, sorghum/bagasse moves processively from left to right and water right to left until
enough juice and bagasse have accumulated to perform hydrolysis and fermentation.

Figure 21: Extraction setup showing the stage # and the direction of water/juice and
sorghum/bagasse flow

87

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88

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Eggleston, G., Cole, M., & Andrzejewski, B. (2013). New Commercially Viable Processing
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Eggleston, G., MONGE, A., & OGIER, E. E. (2003). Sugarcane factory performance of cold,
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alkaline pre-extraction with alkaline-oxidative post-treatment of corn stover to enhance
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Palmqvist, E., & Hahn-Hagerdal, B. (2000). Fermentation of lignocellulosic hydrolysates. II:
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Rooney, W. L., Blumenthal, J., Bean, B., & Mullet, J. E. (2007). Designing sorghum as a
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Chapter 3 – Impacts of delignification and hot water pretreatment on the water induced
cell wall swelling behavior of grasses and its relation to cellulolytic enzyme hydrolysis and
binding
This work has been published as original research in February 2014, in Cellulose,
Volume 21, Issue 1, and Pages 221-235.
Introduction
The majority of terrestrial carbon in the biosphere is thought to be sequestered within
plant cell walls (Gilbert 2010). However, this vast resource of reduced carbon is used primarily
by humans for its existing structural value, as a fuel for combustion, or as a ruminant feed rather
than for the value contained in its existing chemical constituents. This is due to the recalcitrance
of the cell wall to deconstruction by chemical and biological treatments which is set by features
that are both structural and chemical and cut across the length scales at the molecular,
macromolecular, and cellular levels (Ding, Liu et al. 2012). One route for the production of
biofuels is via the biological conversion of plant cell wall polysaccharides through a
pretreatment, enzymatic hydrolysis of the cell wall polysaccharides, and fermentation of these
monomers to a biofuel such as ethanol (Alvira, Tomas-Pejo et al. 2010). The enzymatic
hydrolysis of cell wall polysaccharides requires that an active cellulolytic enzyme be able to
penetrate into the cell wall, bind to its substrate, and perform catalysis. Fundamentally, this can
be considered as a combination of the related properties of both porosity and target glycan
accessibility.
The quantification of porosity in combination with knowledge of other cell wall
properties should yield important information about the enzymatic digestibility and glycan
accessibility. However, compared to other properties such as bulk composition, cell wall
91

porosity and indirect measures of glycan accessibility have received significantly less attention
primarily due to the challenges relating to their accurate quantification. An important concept
when considering the porosity of plant cell walls is that, as a matrix of polymers crosslinked
primarily by non-covalent forces, these porous structures act as swellable hydrogels (O'Neil and
York 2003), particular in unlignified or low-lignin tissues. As such, the solvent properties
influence the swelling of the cell wall and hence the porosity and glycan accessibility to
enzymes. A variety of techniques are available for quantifying porosity and surface area in
porous materials (Papadopoulos, Hill et al. 2003), however techniques for plant cell wall
porosimetry have more restrictions due to the requirement for a hydrated material to maintain
pore integrity (Pönni, Vuorinen et al. 2012). As such, common techniques such as BET or
mercury intrusion are unsuitable, while methods that can be performed in the hydrated state
include differential scanning calorimetry of “bound” water freezing point depression (Yu, Jameel
et al. 2011), solute exclusion (Thompson, Chen et al. 1992; Ishizawa, Davis et al. 2007), and 1H
NMR by either cryoporosimetry (Ishizawa, Davis et al. 2007), diffusion (Topgaard and
Söderman 2001), or relaxation (Andreasson, Forsström et al. 2005). Complementary to these, a
number of methods provide metrics for the strength of water association with plant cell walls
including water retention value (WRV) (Scallan and Carles 1972; Maloney, Laine et al. 1999),
settling volume (Alince and Robertson 1974; Hubbe and Heitmann 2007), “freeness” or the rate
of water drainability (Helmerius, von Walter et al. 2010), and water activity (Fardim, Liebert et
al. 2013; Selig, Thygesen et al. 2013).
The characterization of porosity in delignified woody plant cell walls generated by
chemical pulping, cell wall swelling, and the diffusion of charged cationic or amphiphilic
polyelectrolytes into pores for paper “sizing” has been covered in the literature in the context of
92

chemical pulping and papermaking for the role these phenomena play in influencing the
drainability of delignified pulps, fiber-fiber adhesion, wettability or hydrophobicity of paper, and
the colloidal stability of process liquors among many others (Scallan 1983; Scallan and
Tigerström 1992; Hubbe and Heitmann 2007). In the context of cellulosic biofuels, pretreated,
delignified, and or mechanically refined woody biomass fibers have been characterized with
respect to wet porosity (Grethlein 1985; Thompson, Chen et al. 1992; Koo, Treasure et al. 2011;
Yu, Jameel et al. 2011; Wang, He et al. 2012) and water retention (Luo and Zhu 2011; Luo, Zhu
et al. 2011; Wang, He et al. 2012; Hoeger, Nair et al. 2013; Jones, Venditti et al. 2013), and these
properties have generally been shown to be strongly correlated to the enzymatic yield of glucose.
Relative to the cell walls of woody biomass, which comprise the most industrially significant
fiber source, less literature is available on the porosity and swellability of the cell walls of
graminaceous monocots (grasses) that include some of the most promising feedstocks for
bioenergy processes (e.g. corn stover, switchgrass, miscanthus, etc.) with several publications
characterizing changes in wet-state porosity as a function of pretreatment condition for corn
stover (Ishizawa, Davis et al. 2007) and sugar cane bagasse (Junior, Milagres et al. 2013) while
one study investigated the WRV of dilute acid-pretreated and enzymatically hydrolyzed corn
stover with inconclusive results (Roche, Dibble et al. 2009).
Relative to the cell walls of woody plants, the grasses have significantly different
compositions and organizations at the cellular and macromolecular levels (Ding, Liu et al. 2012).
For example, parenchymatous tissue comprises a significant fraction of the pith of grasses such
as corn stover and sugar cane bagasse that is not present in wood (Lois-Correa 2012). These
tissues are known to be low in lignin, thin-walled, and substantially more digestible by rumen
microbiota (Akin, Rigsby et al. 1993; Wilson, Mertens et al. 1993) as well as cellulolytic
93

enzymes following a pretreatment (Zeng, Ximenes et al. 2012) or a chemical delignification
(Ding, Liu et al. 2012) than the other cell types in grasses. These same tissues are typically
removed in a “de-pithing” step when non-wood fibers such as sugar cane bagasse are used as a
feedstock for chemical pulping due to their poor strength properties and extreme hygroscopicity
(Zanutti 1997) which results in poor drainability of pulped fibers during papermaking. It has
been shown that sugar cane bagasse is able to sorb as much as 20 times its weight in water, while
depithed bagasse holds only five times (Lois-Correa 2012). Other work has shown that corn
stover pith has higher equilibrium moisture contents (Igathinathane, Womac et al. 2005;
Igathinathane, Womac et al. 2007) than either the leaves or rinds over the entire range of relative
humidities. Early work with alkaline hydrogen peroxide (AHP) delignification of wheat straw
demonstrated that nearly completely delignifed wheat straw was capable of retaining three times
more water than untreated wheat straw when subjected to filtration (Gould 1985).
From this it is clear that plant cell wall water swellability, lignin content, and enzymatic
digestibility should be strongly correlated properties, yet these relationships have not been
systematically explored, particularly for potentially important bioenergy feedstocks such as the
grasses. For this work, we investigate these relationships by employing a range of pretreated
grasses that include combinations of alkaline hydrogen peroxide (AHP) delignification and
liquid hot water (LHW) pretreatment that result in significant alterations of cell wall properties
and subsequent enzymatic hydrolysis yields. These sets of untreated and pretreated plant cell
wall materials are characterized with respect to their WRV and the settling volume and
correlated to enzymatic glucose yields and enzyme binding. Additionally, the impact of AHP
delignification on water activity during dynamic vapor sorption will be shown for corn stover as

94

well as significant differences in carboxylic acid contents for select materials, while FTIR is
employed to yield information about compositional changes.
Materials and Methods
Biomass
The biomass feedstocks used in this work included switchgrass (Panicum virgatum, cv.
Cave-in-Rock) and corn stover (Zea mays L Pioneer hybrid 36H56) as reported in our previous
work (Li, Foster et al. 2012). The biomass was milled to pass a 5 mm screen (Circ-U-Flow
model 18-7-300, Schutte-Buffalo Hammermill, LLC) and air-dried to a moisture content of ~5%
before any treatments were performed. The composition of structural carbohydrates and lignin
of all material was determined by the NREL / TP 510-42618 protocol with minor modifications
as described by Li et al. (Li, Foster et al. 2012).
LHW Pretreatment
LHW pretreatment of corn stover and switchgrass was performed in a 5 L M/K Systems
digester (M/K Systems, Inc., Peabody, MA). A total of 500 g of biomass (dry basis) was loaded
into three cylinders (7 cm diameter x 35 cm height) fabricated from 200 mesh corrosion-resistant
304 stainless steel cloth (McMaster-Carr Inc., Cleveland, OH), which were then placed into the
digester with 4 L of water. The digester was then programmed to heat up to 160°C at a heat rate
of 0.8°C /min, holding for 5 min, followed by cool-down for one hour at a rate of ~1°C /min.
The pretreated biomass was washed by soaking in clean water using a large bucket while the
biomass was still inside the stainless steel containers. The solids were then air-dried and
composition analysis was performed prior to AHP delignification and enzymatic hydrolysis.

95

AHP Delignification
Delignification of corn stover and switchgrass, both untreated and LHW pretreated, was
performed using four different conditions of hydrogen peroxide to biomass loadings, 0%, 6%,
12.5% and 25% (g H 2O2 /g biomass). All four conditions were performed in duplicate using 8 g
of biomass (dry basis) at 15% (w/v) which is equivalent to 12.6% to 13% (w/w) depending on
the H2O2 loading condition. Samples were prepared in 250 mL Erlenmeyer flasks and placed in
an incubator at 30°C with shaking at 180 rpm. The flasks were sealed with parafilm to prevent
evaporation but to allow for some expansion as the pressure in the flasks increased with O 2
evolution. The pH was adjusted back to 11.5 during pretreatment at 3, 6, and 9 hours with
aliquots of 5 M NaOH as the pH would decrease during the process for the conditions used.
Delignification was stopped after 24 hours by diluting the sample to 10% (w/w) solids and
adjusting the pH to approximately 4.8 using concentrated sulfuric acid in preparation for
enzymatic hydrolysis.
Enzymatic Hydrolysis
Hydrolysis of all corn stover and switchgrass samples was performed by diluting the
AHP delignification slurries with 25 mL of water to 10% (w/w) solids and adjusting the pH to
approximately 4.8 using concentrated sulfuric acid as mentioned above. Then an aliquot of 1 M
citric acid buffer was added to give a concentration of 50 mM citric acid buffer in the sample
flasks. The antibiotics tetracycline and cyclohexamine were added to make a concentration of 10
mg/L each to prevent microbial contamination. Next, an enzyme mixture of Accelerase 1000,
Multifect Xylanase and Pectinase (Genencor, Inc., Palo Alto, CA) was added in a protein mass
ratio of 4.4:1.7:1, respectively, at an enzyme loading of 30 mg enzyme/g glucan; this optimized
enzyme ratio was determined by Banerjee et al. (Banerjee, Car et al. 2010) and the protein
96

contents of the enzymes were based on the Bradford assay. Samples were then mixed well and
placed in a shaking incubator at 50°C with 180 rpm shaking for 7 days. Sugar concentrations in
the hydrolysate were determined by HPLC using the method described in the NREL / TP 51042618 protocol and converted to glucose yields based on the solids content in the rea ction vessel
and glucan content in the undelignified biomass. Glucose yield is percent of glucose released per
glucose in the undelignified biomass.
Water Retention Value
Water retention value (WRV) was determined according to a modified version of TAPPI
UM 256. For this the biomass samples were filter-washed with a fabricated Buchner funnel
containing a 200 mesh stainless steel screen at the bottom as the porous base. The solids
remaining after delignification were washed with approximately 700 mL of deionized water and
vacuum-filtered to a moisture content of approximately 80%. Next, approximately 2.5 g of this
wet biomass was inserted into a spin-column (Handee Spin Column Cs4, Thermo Scientific)
modified to have a 200 mesh stainless steel screen as the membrane directly under the biomass.
The spin columns were then centrifuged at 900 x g for 15 min (the TAPPI method uses 30 min.)
The drained biomass was then weighed in an aluminum tray and placed in an oven at 105°C for
3 hours, and then weighed again. The WRV is the ratio of the mass of water remaining in the
biomass after centrifuging divided by the mass of dry biomass. Samples were measured in
triplicate and errors bars represent standard deviations.
Settling Volume
The AHP delignified slurries were filter-washed on the fabricated Buchner funnel as
described above. Approximately 0.5 g of the wet solids remaining were transferred to a 20 mL
scintillation vial and approximately 7 mL deionized water was added to achieve a solids
97

concentration of 6.5% w/w accounting for the moisture content of the wet solids. The vials were
vortexed for 30 seconds and allowed to settle for 1 hour. The height of the settled solids and the
height of solids and liquid slurry were measured, and the settling volume was determined as the
ratio of the height of solids to the height of total slurry as reported in the literature (Riedlberger
and Weuster-Botz 2012). Samples were measured in duplicate and the error bars represent the
two values measured.
Enzyme Binding
AHP delignified corn stover slurries were filter-washed on a fabricated Buchner funnel as
described above. Wet solids remaining were then air-dried for several days. Next, 0.5 g of dried
biomass were placed in a 15 mL centrifuge tube for a total of 5 tubes per sample material. A
combined volume of 10 mL of water, Cellic CTec2 (Novozymes, Bagsværd, DK) and 1 M citrate
buffer at pH 5.5 was added to each tube for a solids concentration of 5% (w/v). The pH of 5.5
was selected because of the findings of Lan et. al. which showed that for lignocellulosic
substrates, the optimal pH range for hydrolysis is between 5.5 and 6.2 (Lan et. al. 2013). The
enzyme loading range in the 5 tubes was 0, 5, 10, 15, and 20 mg CTec2/g biomass. The samples
were then placed in a 4°C cold room for 4 hours in a rotary mixer. Following incubation, protein
in the supernatant was assayed by the Bradford assay (Fischer Scientific) using BSA as a
standard, and corrected for background protein from the biomass by subtracting the absorbance
of the sample with no enzyme added. Unbound protein in the liquid was calculated for each
tube, and the bound enzyme fraction was determined as the difference of this unbound
concentration from the initial enzyme concentration. The fraction of bound enzyme for each
sample material was determined by regressing total enzyme concentration with the bound

98

enzyme concentration, where the slope of the regression line is the fraction of bound enzyme for
each material.
Dynamic Vapor Sorption
Dynamic vapor sorption of untreated and air-dried, pretreated corn stover was performed
at 25°C using an AquaLab Vapor Sorption Analyzer (Decagon Devices, Pullman, WA).
Potentiometric Titration
Corn stover and switchgrass solids after AHP delignification were washed and air-dried
before being milled with a Wiley mill to pass a 40 mesh screen. The method used is similar to
Biliuta et. al. (Biliuta, Fras et al. 2011) where 200 mg milled solid samples were added to a
solution consisting of 5 mL of 0.1 M KCl and HCl and 25 mL of water and the pH adjusted to
2.5. The pH was then titrated using a Brinkmann 716 DMS Titrino from 2.5 to 11 using 1 M
KOH and the carboxylic acid content was determined as the mmol equivalent of KOH added per
g of biomass.
FTIR-ATR
FTIR of solid biomass before and after pretreatment and delignification was performed
using a Perkin Elmer Spectrum One FT-IR spectrometer and the Perkin Elmer Universal ATR
Sampling Accessory. Air-dried biomass samples were first milled using a Wiley mill to pass a
40 mesh screen. Samples were placed on the FTIR diamond and covered with aluminum foil
and the pressure arm was adjusted to 70 bar. Spectra were collected in the transmittance mode
between 650 and 4000 cm-1 at a resolution setting of 4 cm-1 using 16 scans per sample.
Individual spectra were normalized by mean-centering and scaling with respect to the spectra
standard deviation (Robert, Marquis et al. 2005) to better compare spectra between conditions.

99

Results and Discussion
Biomass Pretreatment and Delignification
Combinations of alkaline hydrogen peroxide (AHP) delignification and liquid hot water
(LHW) pretreatment of corn stover and switchgrass were performed in order to generate
materials with a range of compositions, susceptibilities to enzymatic hydrolysis, as well as a
diverse set of other cell wall properties such as water sorption that may allow for the correlations
between these properties to be quantified. The compositions of the 20 materials generated by
this combination of pretreatments and biomass feedstocks are presented in Table 8 along with the
glucose conversions shown in the appendix to this chapter. These data show that AHP
delignification preserves the majority of the cellulose and xylan while solubilizing lignin which
has been well-established in the literature (Gould 1985). The LHW pretreatment is shown to
solubilize xylan while preserving cellulose and lignin, which is also well-known from the
literature whereby the improvement in digestibility can be attributed to xylan removal by
autocatalyzed acid hydrolysis and lignin relocalization due to its increased mobility at elevated
temperature (Selig, Viamajala et al. 2007). This relocalized lignin is known to occlude access to
polysaccharides and has been shown to be overcome by additional, subsequent delignification
(Selig, Vinzant et al. 2009).
Differences in macroscopic appearance of the slurries of 15% (w/v) corn stover and
switchgrass subjected to AHP delignification (12.5% w/w H 2O 2 loading) are presented in Figure
22. These two materials show a striking difference in their apparent hygroscopicity with the
AHP-delignified corn stover, which is substantially more digestible by fungal cellulases,
showing essentially no free water while the AHP-delignified switchgrass solids are able to settle
out of the slurry. A number of cell wall properties, both structural and compositional, can be
100

hypothesized to contribute to these differences in water swelling behavior. Water is known to be
associated through a number of chemico-physical phenomena with plant cell wall biopolymers
and the exterior and interior surfaces of cell walls as “free” and “bound” water. Free water
comprises bulk water in large pores such as the lumen, and may strongly resist removal by, for
example, capillary forces. Bound water represents more thermodynamically constrained water
involved in non-covalent chemical interactions with cell wall biopolymers. This bound water
can consist of primary bound water or “non-freezing” water that is tightly associated with
cellulose surfaces, even within cellulose crystalline regions (Matthews, Skopec et al. 2006).
Secondary bound water exhibits freezing point depression, but is still capable of undergoing a
solid-liquid phase change. The relative abundance of these pools of water is set by the overall
surface area and porosity of the cell wall matrix and its local chemical environment.

Figure 22: Observable macroscopic differences in the water swelling behavior of AHPdelignified (A) corn stover and (B) switchgrass at 15% (w/v) solids content.

Foremost among the compositional differences are lignin content and accessible
amorphous polysaccharide content. Polysaccharide-associated bound water is thought to be most
abundant in association with amorphous polymers (e.g. hemicelluloses or amorphous regions of

101

cellulose), and it is known to decrease proportionally with cellulose crystallinity (Nakamura,
Hatakeyama et al. 1981; Hatakeyama, Nakamura et al. 2000). For lignin, both its total content
and location strongly impact cell wall enzymatic digestibility (Grabber, Hatfield et al. 2003;
Ding, Liu et al. 2012). Prior to lignification, the cell wall matrix behaves as a porous, swellable
hydrogel, becoming hydrophobic and water-excluding after lignification (O'Neil and York
2003). It is well-established that delignification and hemicellulose removal can increase cell wall
matrix porosity, allowing for increased water penetration and water swelling (Grethlein 1985;
Akinli-Kogak 2001), and that these water-swellable, nano-scale pores are thought to exist as the
voids between delaminated microfibril sheets (Fahlén and Salmén 2004). As cell wall matrix
“hydrophilicity”, swellability, and porosity are clearly important properties relating to
polysaccharide accessibility to cellulolytic enzymes, these water properties are the subject of
further investigation in this work.
Water Retention Value and Settling Volume
Quantifiable metrics of cell wall-water association, such as water retention value (WRV),
incorporate many other properties such as cell wall composition and porosity, and may be a
useful, simplified indicator of enzymatic digestibility. The variation in the WRV as a function of
centrifugation speed was explored for untreated corn stover and switchgrass and AHPdelignified corn stover and switchgrass using the 12.5% H 2O2 loading condition. These results
are presented in Figure 23 and show a number of results of interest. The first observation is that
AHP delignification more than doubles the amount of water that the biomass can hold. This
substantial increase in water swelling has been identified for AHP-delignified wheat straw
(Gould 1985). Figure 23 also shows that even though WRV decreases with increasing speed, the
decrease is less for untreated material than for AHP-delignified material. Untreated corn stover
102

and switchgrass decrease from approximatly 1.5 to 1.2 while AHP delignified material decreases
from 3.5 to 2.5. Similar results have been shown for cotton, where higher centrifuge speeds
removed more water (Aggebrandt and Samuelson 1964). However, shorter centrifuge times will
also reduce the amount of water removal. In fact, the SCAN-C 62:00 method for WRV uses a
spin speed of 3000 x g for 15 min compared to the TAPPI UM 256 method of 900 x g for 30
min.

Figure 23: Water retention value (WRV) for corn stover (CS) and switchgrass (SG) before and
after AHP delignification as a function of centrifugation speed for 15 min centrifugation time

A second notable observation from Figure 23 is that water swelling values tend toward an
asymptote as the centrifugation speed is increased for both untreated and AHP delignified corn
stover and switchgrass. This is likely due to easy-to-remove free water being largely removed
before some threshold centrifugation speed and the only water remaining is bound water that is
more strongly associated with the biomass. A “fiber saturation point” has been proposed to
represent the moisture content at which there is no free water remaining within the cell lumina,
and all remaining water is contained in the cell wall, and previous work has proposed that the
WRV is a good estimate of the fiber saturation point (Scallan and Carles 1972). However,

103

certain properties of the bulk biomass material can impact water drainage suggesting that the
WRV may not be such a good estimate of the fiber saturation point. For example, a higher
packing density would be expected for material with a wider particle size distribution, and thus
these materials would be able to trap more water in the biomass matrix. Also, the amount of
small particles, or fines, can impact water drainage by blocking water flow pathways and
increasing the water holding capacity; this is termed the choke-point hypothesis (Hubbe and
Heitmann 2007).
A third observation from Figure 23 is that for all centrifugation speeds, the untreated and
pretreated corn stover consistently retains more water than the corresponding untreated and
pretreated switchgrass. These differences are statistically significant for all but three conditions
using a t-test with an α of 0.05 (data not shown). These findings confirm the observations
presented in Figure 22. For the remainder of this work, the lowest centrifugation speed tested,
900 x g, was employed to have more sensitivity in the measurements allowing larger differences
between samples to be observed. Additionally, this is the standard centrifugation conditions for
the established WRV protocols (TAPPI), albeit with a centrifugation time of 30 minutes rather
than 15 minutes used in this work.
WRV and settling volume were next determined for corn stover and switchgrass samples
that were subjected to a range of pretreatment conditions including combinations of liquid hot
water (LHW) pretreatment and AHP delignification. These diverse pretreated samples showed
enzymatic glucose yields after 7 days of hydrolysis ranging from 18% to 85%. The correlations
between these two metrics and enzymatic glucose yield are shown in Figure 24. These
correlations between the glucose yields settling volume and WRV both have a compelling linear
correlation, R2 of 0.895 and 0.900 respectively, regardless of biomass feedstock, pretreatment
104

type, or pretreatment condition. This is noteworthy because correlating enzymatic digestibility
to carbohydrate or even lignin content does not give a clear trend for these samples (data not
shown) and is therefore inconclusive as to the relative contributions of these factors toward
impacting sugar yield.

Figure 24: Corn stover (CS) and switchgrass (SG) correlation between the enzymatic glucose
yield and (A) settling volume and (B) WRV

Differences in composition may be one (of many) of the factors that contributes to the
differences in water swelling. Lignin has been shown to have the lowest water affinity followed
by cellulose and then hemicelluloses in lignocellulosic material (Berry and Roderick 2005).
However, Weber et al. have shown that cellulose does not contribute as much to WRV as
hemicelluloses and lignin, and they did not observe a correlation between hemicellulose content
and WRV (Weber, Kohlhepp et al. 1993); this may be because other properties of the
hemicelluloses are also important factors that contribute to water retention (Lund, Sjostrom et al.
2012). As factors such as porosity and surface affinity are neglected by composition-only
information, the increased settling volume and WRV may provide indirect evidence for
increased porosity and polysaccharide accessibility to enzymes.
105

Settling volume has been studied extensively in the context of compaction and settling of
activated sludge (Jenkins, Richard et al. 2004; Jones and Schuler 2012). For fibrous biomass,
particle settling volume is an alternative quantification of the biomass bulk density which has
been reported for bioenergy feedstocks such as wheat straw and switchgrass (Lam, Sokhansanj et
al. 2008), with particle size, individual particle density, and particle-particle interaction being
important in controlling the bulk density. The settling volume provides a metric for
distinguishing differences between inter-fiber friction, and chemical treatments that increase
settling volume also tend to increase dewatering rates (Hubbe and Heitmann 2007).
Enzyme Adsorption
Enzyme adsorption isotherms were determined for corn stover subjected to increasing
AHP delignification (Figure 25A), with enzyme adsorption determined at 4°C to minimize
catalytic activity. The obvious trend from this data is that the substrate has a higher binding
affinity for cellulases with increasing delignification. As these are not complete isotherms and
only represent the portion of the curve at low enzyme loadings before the adsorption saturates,
they show an approximate linear increase in enzyme adsorption with increasing loadings to
which linear trendlines are fitted. The slope of this portion of the isotherm is taken as the
fraction of the protein that is bound to the biomass (mass bound protein / mass total protein).
These values are plotted against the WRV and enzymatic glucose yields in Figure 25B which
shows that there is a strong correlation between the bound enzyme fraction and WRV, R 2 =
0.995, and the enzymatic glucose yield, R2 = 0.914. This result for increasing enzyme adsorption
with increasing enzymatic hydrolyzability has been shown many times in the literature for many
types of biomass and treatments for complex cellulase cocktails. As examples, strong
correlations between protein adsorption and enzymatic glucose yield were found for
106

mechanically fibrillated softwood Kraft pulps (Hoeger, Nair et al. 2013), AHP-delignified rice
straw (Wei and Cheng 1985), as well as many other diverse alkaline and acid pretreatments of
corn stover (Kumar and Wyman 2009).

Figure 25: Impact of increased WRV on (A) enzyme binding and (B) the correlation between
bound enzyme fraction and WRV and enzymatic glucose yield.

It should be considered that this is a multicomponent enzyme cocktail with many proteins
with strongly differing substrate affinities and even some catalytic components such as βglucosidase that do not contain cellulose binding modules (CBMs) and would not be expected to
show strong adsorption to insoluble cell wall polymers. Additionally, protein adsorption is not
necessarily correlated to the sugar yields as non-specific adsorption of enzymes to lignin is
known to be important, in particular to high-lignin materials such as acid-pretreated softwoods
(Tu, Pan et al. 2009).
Dynamic Vapor Sorption
Dynamic vapor sorption (DVS) was employed as a method to investigate the impact of
AHP delignification on the water affinity of surfaces within the porous cell walls, how this might
107

relate to structural and chemical changes in the biomass, and how this could be interpreted with
respect to differences in the enzymatic digestibility of these materials. A substantial literature
exists for vapor sorption and its dynamics in native and modified woody biomass as reviewed by
Engelund et al. (Engelund, Thygesen et al. 2013). However, the literature for grasses is less
well-developed. For example, sorption isotherms (for adsorption only) have been reported for
corn stover components (Igathinathane, Womac et al. 2005; Igathinathane, Womac et al. 2007),
big bluestem (Karunanithy, Muthukumarappan et al. 2013), wheat straw, and reed canary grass
(Nilsson, Svennerstedt et al. 2005). Adsorption-desorption hysteresis and the effect of cell wall
modifications such as pretreatment on these sorption dynamics for grasses have not been
reported in the literature. In this work, DVS isotherms at 25˚C were determined for untreated
corn stover and AHP-delignified corn stover (12.5% H2O2 loading) with the results presented in
Figure 26. These show changes in the moisture content of the material as a function of the
relative humidity (alternatively the water activity, a w, of the sorbed water) for water adsorption
(lower curves) and desorption (upper curves).

108

Figure 26: Water activity isotherms for untreated and AHP delignified corn stover

The data in Figure 26 show at least three notable results that deserve comment. The first
result is that the AHP-delignified corn stover has a lower water activity than the untreated
material for any given moisture content. In other words, the water vapor adsorption-desorption
isotherms are shifted to the left following delignification. This indicates that following mild
delignification, more water is present at any select moisture content in a more constrained
environment. This can be interpreted as indicating that more water can be adsorbed within the
cell wall pores (or on fiber surfaces) and that delignification may increase the accessible sites for
water to sorb by increasing intra-cell wall porosity. The oxidative delignification may
additionally introduce more hydrophilic functional groups such as carboxyl groups as a
consequence of oxidation reactions, or increasing the accessibility hydroxyl groups such as in
polysaccharides and in particular with non-crystalline hemicelluloses (Olsson and Salmen 2004).
109

DVS of different cellulose allomorphs has shown that native allomorphs of cellulose (cellulose I)
offer significantly less sorption potential than other allomorphs (Selig, Thygesen et al. 2013).
However, alkali concentrations used for AHP delignication in this work are not at a high enough
concentration to induce the formation of the cellulose II allomorph. The second notable result
from Figure 26 is that the total mass of sorbed water at 100% relative humidity (a w of 1.0) is
increased, with the equilibrium moisture content increasing from 23.7% to 25.5% following
AHP delignification, again indicating a higher affinity for water. The third notable finding is that
the AHP delignified corn stover also has smaller hysteresis than the untreated material. Water
sorption/desorption hysteresis in swellable, porous materials has been attributed to irreversible
water phase change, irreversible absorbent swelling, and capillary condensation-evaporation
processes in pores where vapor condenses in the pores and is then trapped (Boki and Ohno
1991). Small hysteresis may be the result of weakly associated fiber chains and weakly bound
water for material with high swelling and sorption properties, or for smaller swelling and
sorption properties the opposite is true, indicating strongly associated chains and bound water.
This would make sense for AHP delignified corn stover which is more enzymatically digestible
than untreated corn stover, and weaker polymer and water association would allow for easier
enzyme access and subsequent hydrolysis of sugar polymers for the AHP material. However,
hysteresis differences may also be explained by differences in mesopore volume (Boki and Ohno
1991) which too could explain the differences between untreated and AHP delignified corn
stover, where lignin and hemicelluloses have been removed with the AHP treatment, potentially
increasing pore volume and allowing, again, for more enzyme access.

110

Cell Wall Carboxylate Content
Oxidizing pretreatments and pulp bleaching processes are well-known to introduce
carboxylate groups to cell wall biopolymers. It is presumed that these functional groups may
influence the chemical and structural environment within the cell wall matrix, for example, by
increasing electrostatic repulsion between polymers, increasing the affinity for water within the
cell wall, and resulting in an overall more porous cell wall when swollen with water. The
carboxylic acid contents of AHP-delignified corn stover and switchgrass were determined by
potentiometric titration with the results presented in Figure 27. This shows that estimated acid
content is higher for corn stover (0.58-0.77 mmol COOH/g original biomass) relative to
switchgrass (0.38-0.64 mmol COOH/g original biomass) for all conditions. Another result is that
the carboxylic acid content passes through a maximum at an H 2O2 loading 12.5% (w/w on
biomass) after which it decreases.

111

Figure 27: The carboxylic acid content determined by potentiometric titration as a function of
AHP pretreatment condition on (A) a per mass biomass and (B) a per mass residual lignin basis
and as a function of enzymatic glucose yield on (C) a per mass biomass and (D) a per mass
residual lignin basis

Carboxylic acid groups are known to be sites for water adsorption; therefore, these data
corroborate the water swelling capacities above with the exception of the 25% condition. This
may be due to the fact that lignin content has been reduced so much that cellulose chains are free
to swell much less inhibited than the other conditions regardless of the amount of water
adsorption sites. A similar result was found by Scallan et al. where an increase in acid groups to
a maximum was observed following selective delignification by Kraft pulping, then it decreased
again as progressively more lignin was removed (Scallan and Tigerstrom 1992). It was
112

hypothesized that water will imbibe the cell wall due to osmotic pressure causing the cell wall to
swell; swelling stops when osmotic pressure equals the elastic tension of the cell wall. Increased
delignification by Kraft pulping has been shown to decrease elastic modulus which equates to
increased swelling, and at some point a threshold was reached where lignin removal would no
longer decrease the elastic modulus because of an increase in hydrogen bonding between
cellulose fibers which counteracted the effect of lignin removal (Scallan and Tigerstrom 1992).
Figure 27B shows the same results plotted per g lignin with the assumption that the majority of
the COOH groups are introduced onto the lignin. For this, the trend is a continuous increase in
COOH/g lignin with increasing H2O2 loading indicating that the introduction of COOH groups is
indicative of lignin removal. Plotting this same data against enzymatic hydrolysis yields of
glucose shows similar trends (Figure 27C, D). Specifically, the glucose hydrolysis yields show
maxima (Figure 27C) relative to a carboxylate content on a per original biomass basis while a
linear trend is apparent for glucose hydrolysis yields when plotted against carboxylate content on
a per mass lignin basis.
FTIR-ATR
In order to understand the impact of AHP delignification and LHW pretreatment on other
chemical features of corn stover and switchgrass that may contribute to water swelling, the
samples were analyzed using FTIR-ATR. These results, plotted in Figure 28A, show a select
region of the FTIR spectra subtracted from the spectra of untreated material for AHP delignified
corn stover over the range of H 2O2 loadings used. Peaks below 0 represent decreases in
transmittance intensity for that peak from untreated material, while peaks above 0 represent
increases in intensity. Peak assignments to specific functional groups and properties in biomass
are difficult as even in the literature there seem to be differences depending on experiments and
113

materials. There are notable decreases in transmittance for peaks 1727, 1386, and 1235 which
have been attributed to acetyl esters in xylan (Sun, Sun et al. 2004; Wrigstedt, Kylli et al. 2010)
as well as ferulate and p-coumarate esters (Pan, Bolton et al. 1998), and therefore are indicative
of saponification of these esters, which is expected considering the alkaline pH used during
delignification. The decrease in peak 1517 indicates a decrease in lignin which is in agreement
with the composition results after delignification (see Table 8) (Liu, Xu et al. 2006). Ferulate
and p-coumarate may contribute to this peak (Nakagame, Chandra et al. 2011), which would
agree with results in our lab showing an increase in ferulic and p-coumaric acid removal with
increasing AHP severity (manuscript in preparation). The peak at 1640 represents water bending
vibration (Lojewska, Miskowiec et al. 2005), showing a decrease with increasing AHP severity;
this makes sense as the air-dried moisture content also decreases with increasing AHP severity
due to more severe hornification because of more lignin and hemicelluloses removal. The peak
at 1160 is antisymmetric bridge stretching of C-O-C groups typically for the xylan backbone
(Cao and Tan 2004; Wrigstedt, Kylli et al. 2010) and the peak at 990 is for β-glycosidic bonds
associated with cellulose (Robert, Marquis et al. 2005) indicating a decrease in xylan content and
an increase in cellulose content which is corroborated by composition data (Table 8). The peak at
1710 may be due to carboxyl or aldehyde functionality (Lojewska, Miskowiec et al. 2005) and
has been attributed to carboxylic acid groups in lignin, which is in agreement with the results in
Figure 27. Figure 28B shows the same results for LHW pretreated followed by AHP delignified
corn stover. There is noticeably less cellulose concentrating evinced by the smaller peak height
at 990, which agrees with composition data.

114

Figure 28: FTIR spectra of AHP delignified corn stover compared to (A) untreated material and
to (B) LHW treated material. Results show transmittance deviation for each delignified material
from the untreated material or LHW-only spectra which were used as the baselines

Conclusions
Cell wall water swelling and particle settling volume are measurements that are impacted
by and can provide indirect assessments of biomass properties such as porosity and composition
which have independently been shown to correlate well with enzyme accessibility and/or enzyme
binding and, therefore, enzymatic digestibility. This work has shown that for combinations of
LHW pretreatment and AHP delignification, two different biomass materials, corn stover and
switchgrass having a range of compositions and water sorption behavior, WRV and settling
volume are strong predictors of enzyme binding and enzymatic conversion. This is reasonable as
water molecular association with biomass would be constrained by the same biomass properties
as limit enzymatic deconstruction plant cell walls: polysaccharide accessibility, porosity, and
surface water affinity. This work also showed that mild AHP delignification increased WRV up
to 3-fold by removing lignin and increasing the water swellability of cell walls. It was also
shown that AHP delignification increased the carboxylic acid content of corn stover and

115

switchgrass under some oxidative delignification conditions, which may also contribute to the
increased cell wall affinity for water. Dynamic vapor sorption isotherms demonstrated that
AHP-delignified corn stover exhibited an increased capacity for water sorption from the vapor
phase relative to untreated corn stover, with more water present at any moisture content present
in a more constrained environment, indicating more water-accessible pore volume. FTIR results
corroborated what was known from composition results, that AHP delignification removed lignin
and xylan and consequently concentrated the composition of cellulose. Future work will
investigate a greater range of pretreatment technologies and pretreatment conditions to determine
if this predictive power is universal and could be a valuable tool for evaluating pretreatment
effectiveness. If this were to be the case, WRV, would be a very useful screening tool for
identifying biomass feedstocks that would be more susceptible to chemical and enzymatic
deconstruction.
Acknowledgements
This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER
Office of Science DE-FC02-07ER64494). The authors are grateful to Robert and Brady Carter
at Decagon Devices (Pullman, WA) for performing DVS on samples. Natasse Christides and
Genevieve Gagnier provided laboratory assistance and were supported by an NSF Due Grant
(#0757020).

116

APPENDIX

117

Appendix: Supplimental data and a description of apparatus for measuring WRV
Table 8: Glucan, xylan, lignin compositions and glucose yields of corn stover and switchgrass
before and after AHP delignification and LHW pretreatment

Glucan
Xylan
Klason
Lignin
Glucose
Yield

Glucan
Xylan
Klason
Lignin
Glucose
Yield

Corn Stover
Untreated AHP Delignified (g
0
0.06
g/g
g/g
33.2%
37.5%
37.0%
± 0.7%
± 0.1% ± 1.0%
21.5%
22.7%
23.5%
± 0.1%
± 0.3% ± 0.4%
20.5%
19.0%
16.8%
± 0.6%
± 0.4% ± 1.3%
26.4%
48.2%
51.0%
± 0.7%
± 0.1% ± 0.2%

H 2 O2 / g biomass)
0.125
0.25
g/g
g/g
44.0%
48.5%
± 0.4% ± 0.4%
23.7%
25.2%
± 0.4% ± 0.4%
12.9%
5.9% ±
± 1.6%
1.8%
64.6%
84.7%
± 0.2% ± 0.9%

Switchgrass
Untreated AHP Delignified (g
0
0.06
g/g
g/g
27.4%
33.7%
36.6%
± 0.3%
± 0.1% ± 0.4%
20.5%
25.2%
27.0%
± 0.1%
± 0.1% ± 0.1%
21.9%
19.3%
18.2%
± 0.3%
± 0.4% ± 1.2%
15.2%
20.1%
29.9%
± 0.5%
± 0.1% ± 1.3%

H 2 O2 / g biomass)
0.125
0.25
g/g
g/g
37.5%
45.0%
± 0.5% ± 0.5%
28.0%
24.7%
± 0.3% ± 0.2%
14.3%
8.9%
± 0.1% ± 0.4%
47.8%
52.2%
± 0.2% ± 1.2%

Liquid Hot Water Pretreated
40.9%
37.7%
46.2%
± 1.4%
± 0.6% ± 2.5%
20.2%
18.5%
20.3%
± 0.3%
± 0.8% ± 0.6%
25.5%
25.3%
21.0%
± 2.3%
± 1.3% ± 1.5%
53.9%
58.0%
60.9%
± 1.3%
± 0.9% ± 0.1%

51.7%
± 0.1%
20.3%
± 1.3%
20.2%
± 0.6%
74.2%
± 0.2%

39.5%
± 0.1%
22.4%
± 0.1%
23.3%
± 0.8%
27.7%
± 0.2%

46.3%
± 0.6%
20.9%
± 0.5%
16.6%
± 0.2%
47.0%
± 0.2%

63.1%
± 0.8%
17.7%
± 0.2%
8.3% ±
0.3%
87.5%
± 0.1%

31.8%
± 0.0%
18.3%
± 0.0%
19.6%
± 0.2%
31.4%
± 0.4%

43.1%
± 0.5%
23.3%
± 0.3%
20.6%
± 0.2%
38.1%
± 0.3%

53.6%
± 0.1%
18.9%
± 0.1%
11.5%
± 0.4%
54.3%
± 0.4%

The Buchner funnel and spin column used for WRV quantification described above are
shown in Figure 29 below. The diameters of the 200 mesh porous base and spin column
membrane are 9 cm and 1.25 cm respectively. These dimensions are important and may be
responsible for any differences in results obtained when using other equipment. Different
equipment for biomass pad formation prior to centrifugation have been shown to yield different
values of WRV in the SCAN-C 62:00 method.

118

Figure 29: Fabricated Buchner funnel with 200 mesh porous base (left) and modified spin
column with 200 mesh membrane (right)

119

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Chapter 4 – Water Retention Value of Pretreated Grasses Multiple Linear Regression
Models
Introduction
The use of lignocellulosic biomass as a raw material for producing biofuels is an
attractive option due to it being in abundance and a renewable resource. A promising conversion
route to make fuel from lignocellulose is the biochemical pathway which utilizes polysaccharide
hydrolyzing enzymes and fermenting microbes to produce fuels from cell wall carbohydrates.
The challenge is that lignocellulose is difficult to degrade biologically due to its recalcitrant
nature, which is a consequence of the ultrastructure created by its constituent’s cellulose,
hemicelluloses and lignin. Therefore, a pretreatment step is required before adding enzymes to
disrupt the ultrastructure providing the enzymes access to the structural carbohydrates,
promoting cell wall deconstruction. There are many pretreatment technologies being researched
with a variety of chemistries and mechanisms for effective cell wall disruption that include a
range of property changes of the biomass itself (Ong, Chundawat, Hodge, Keskar, & Dale,
2014). Porosity, pore volume and pore size distribution are important physical properties that
can change during pretreatment which allow for increased enzyme accessibility.
Typical enzymes used for cell wall polysaccharide depolymerization include glycoside
hydrolases (Lynd, Weimer, van Zyl, & Pretorius, 2002), monooxygenases (Harris et al., 2010;
Langston et al., 2011; Quinlan et al., 2011), and esterases. Non-complexed fungal and bacterial
glycoside hydrolases typically contain a catalytic region and a binding domain consisting of a
carbohydrate binding module (CBM) from any number of evolutionarily conserved families.
CBMs are necessary to bring the protein glycoside hydrolases into proximity of the substrate and
orienting it for catalysis. CBM binding is thought to disrupt the local H-bonding network in the
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region of the cellulose surrounding the binding site (Boraston, Bolam, Gilbert, & Davies, 2004;
Nigmatullin et al., 2004), while the introduction of hydrolytic chain breaks causes further
disruption (Bu et al., 2009). Enzyme access to binding sites is important and has been known to
be impacted by lignin and hemicelluloses content due to physical occlusion as well as to be
impacted by amorphous cellulose content measured by crystallinity (Chundawat et al., 2011).
Both of these properties are measured independently and it is difficult to use composition data
and crystallinity together to predict improved enzyme binding and thus enzymatic digestibility.
Furthermore, these types of measurements completely omit the contribution of porosity and
substrate surface affinity that would also contribute to enzyme accessibility and binding. Several
techniques exist for performing pore analysis that can be used on biomass including mercury
intrusion (Rigby, Fletcher, & Riley, 2002), gas permeability (Garey, Leekley, Hultman, & Nagel,
1973), scanning electron microscopy (Chinga, Helle, & Forseth, 2002), and atomic force
microscopy (Mohammad, Hilal, & Seman, 2005). However, these methods typically require dry
samples, and for lignocellulosic fibers, drying can cause irreversible collapse of pores, or
hornification, that would not be observed if samples remained wet as they do during the
conversion process. Therefore, pore analysis would be better done with undried samples, and
there are methods for doing this as well, including differential scanning calorimetry (DSC)
(Nakamura, Hatakeyama, & Hatakeyama, 1981; Park, Venditti, Jameel, & Pawlak, 2006), NMR
cryoporometry (Gane et al., 2004), and water retention value (Weise, Maloney, & Paulapuro,
1996). These methods actually utilize water-fiber interactions and relate them to pore properties.
For example, DSC defines three types of water in wet fibers: free water, freezing bound water
and non-freezing bound water (Liu & De Yao, 2001; Nakamura, et al., 1981). Non-freezing
bound water is that which does not crystallize at temperatures below the freezing point of water,

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and it is thought to be the result of more strong hydrogen bonding with polymer surfaces as well
as spatial confinement within “nanocavities” which prevent crystallization of the water (Berlin,
Kliman, & Pallansc.Mj, 1970; Liu & De Yao, 2001). In fact, water molecules will first bind to
polymers at water binding sites preferentially until all sites are occupied, comprising the nonfreezing bound water content, before filling sorption sites for freezing bound water (Ping,
Nguyen, Chen, Zhou, & Ding, 2001). Freezing bound water is that which is contained in pores
and has the property of depressed melting temperatures compared to free water. These depressed
melting temperatures have been correlated to different pore sizes which can be quantified based
on the amount of freezing bound water they contain (Brun, Lallemand, Quinson, & Eyraud,
1977; Park, et al., 2006). The application of these wet-methods for characterizing biomass, in
particular pulp, can be found extensively in the literature.
The interaction between water and pulp fibers has been an important topic in the pulp and
paper industry for a long time for a variety of process related and paper quality related reasons
(Akinli-Kogak, 2001; Weise, et al., 1996). For example, a higher packing density would be
expected for material with a wider particle size distribution, and thus these materials would be
able to trap more water in the biomass matrix. Also, the amount of fine particles can impact
water drainage by blocking water flow pathways and increasing the water holding capacity; this
is termed the choke-point hypothesis (Hubbe & Heitmann, 2007). In addition to these bulk
properties, micro and nano-scale properties are also known to impact water interaction with
biomass. Delignification and hemicelluloses removal have been shown to increase pore volume
and leave behind empty space for water molecules to occupy, which allows for increased water
penetration and thus increased WRV (Akinli-Kogak, 2001; Grethlein, 1985). Water molecules
also absorb to biomass surfaces by hydrostatic interactions with hydroxyl and carboxyl
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functional groups (Olsson & Salmen, 2004). Water properties are also used as indicators of
paper quality; for example, increased fiber swelling is known to improve paper strength (Talwar,
1957). Methods have been developed in the pulp and paper industry to quantify biomass and
water interactions as important indicators of fiber properties. The WRV, settling volume and
freeness of pulp are examples of such methods. The WRV is a centrifugation method where a
pad of wet biomass is drained using a centrifuge at a specified speed and duration; the value is
determined by the amount of water remaining in the amount of biomass after centrifugation.
Settling volume is a good way to view differences between inter-fiber friction and treatments that
increase settling volume also tend to increase dewatering rates (Hubbe & Heitmann, 2007). The
settling volume quantifies the relative height of biomass to total slurry height when it is allowed
to settle in a solution of water at a specified solids concentration and duration. Water activity is
another method for quantifying the interaction of water with biomass and is typically used in the
food industry as a way to predict bacterial growth on food. Water activity is a measure of the
energy state of water bound or trapped in a solid material based on partial pressure of sorbed
water molecules. Our previous work, discussed in chapter 3, has demonstrated the changes in
WRV, settling volume and water activity that take place after AHP delignification and how they
can be related to enzyme digestibility (Williams & Hodge, 2014).
The quantifiable water properties of biomass discussed thus far have been used
extensively for woody biomass in the pulp and paper industry, but have not been used
extensively in the context of lignocellulosic biomass, particularly grasses, for biofuels.
However, some examples include the work of Luo et al which has shown that WRV is a good
indicator of cellulose accessibility after wet-pressed and heat-dried hornification has occurred,
and that enzyme absorption to cellulose substrate correlates well with WRV for woody biomass
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(Luo & Zhu, 2011; Luo, Zhu, Gleisner, & Zhan, 2011). Roche et al used the amount of entrained
liquid (a variation of WRV) to determine if pore volume changed during enzymatic hydrolysis
for corn stover (Roche, Dibble, Knutsen, Stickel, & Liberatore, 2009). Weber et al has shown
that cellulose does not contribute as much to WRV as hemicelluloses and lignin, however, no
correlation between hemicellulose content and WRV was found (Weber, Kohlhepp, Idouraine, &
Ochoa, 1993); this may be because other specific properties of the hemicelluloses may be more
important factors that contribute to WRV, for example hydroxyl and carboxyl contents, or
charged groups which are known to increase fiber swelling (Lund, Sjostrom, & Brelid, 2012).
And cell wall porosity will be indicative of water and enzyme penetration and therefore
enzymatic digestibility. The hypothesis is therefore, that quantifiable water and biomass
interactions may incorporate composition, porosity and hydrophilic properties of the cell wall
and be a more useful indicator of enzymatic digestibility than any one group of these properties
on their own. The work in chapter 3 has shown a positive, linear correlation between WRV and
glucose yield for corn stover and switchgrass subjected to a range of AHP and LHW
pretreatment conditions. This work will expand on our previous findings to include more
extreme AHP and LHW pretreatment conditions, and also include AFEX pretreatments, to
investigate how well WRV can be predictive of glucose yield over a wider range of biomass
properties including composition and WRVs. A multiple linear regression (MLR) analysis will
be performed to determine if the predictive capabilities of WRV can be improved by including
specific biomass composition information where WRV may not be sufficient alone.

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Methods and Materials
Biomass
The untreated biomass feedstocks used in this work include switchgrass (Panicum
virgatum, cv. Cave-in-Rock) and corn stover (Zea mays L Pioneer hybrid 36H56). The biomass
was milled with a Wiley Mini-Mill (Thomas Scientific) to pass a 5 mm screen and air-dried to a
moisture content of ~5% before any treatments were performed. AFEX pretreated switchgrass
and corn stover were obtained from Rebecca Garlock at Michigan State University. The
composition of structural carbohydrates and lignin of all material was determined by the NREL /
TP 510-42618 protocol with minor modifications as described by Li et al. (Li et al., 2012).
LHW Pretreatment
LHW pretreatment of corn stover and switchgrass was performed in a 5 L M/K Systems
digester (M/K Systems, Inc., Peabody, MA) at three different conditions, 160°C for 5 min and 60
min, and 190°C for 5 min. The pretreatment method is the same as described in a previous paper
(Williams & Hodge, 2014). Briefly, a total of 500 g of biomass (dry basis) was loaded into three
cylinders (7 cm diameter x 35 cm height) fabricated from 200 mesh corrosion-resistant 304
stainless steel cloth from McMaster-Carr, which were then placed into the digester with 4 L of
water. The digester was then programmed to heat up to the desired temperature, 160°C or 190°C,
at a heat rate of 0.8°C /min, holding for 5 min or 60 min, followed by a cool-down for one hour
at a rate of ~1°C /min. The pretreated biomass was washed by soaking in clean water for 24
hours and the solids were then air-dried.

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AHP Pretreatment
Delignification of corn stover and switchgrass, both untreated, LHW pretreated, and
AFEX pretreated (switchgrass only) was performed using two different conditions of hydrogen
peroxide to biomass loadings, 2%, and 10% (g H 2O2 /g biomass). Both conditions were
performed in duplicate using 8 g of biomass (dry basis) at 15% (w/v). Samples were prepared in
250 mL Erlenmeyer flasks and placed in an incubator at 30°C with shaking at 180 rpm. The
flasks were sealed with parafilm to prevent evaporation but also to allow for some expansion as
the pressure in the flasks increased with O 2 evolution. The pH was adjusted back to 11.5 during
pretreatment at 3, 6, and 9 hours with aliquots of 5M NaOH as the pH would decrease during the
process for the conditions used. Delignification was stopped after 24 hours by diluting the
sample to 10% (w/w) solids and adjusting the pH to approximately 4.8 using concentrated
sulfuric acid in preparation for enzymatic hydrolysis.
Enzymatic Hydrolysis
Hydrolysis of all corn stover and switchgrass samples was performed by diluting the
AHP pretreated slurries with 25 mL of water to 10% (w/w) solids and adjusting the pH to 4.8
using concentrated sulfuric acid as mentioned above. Then an aliquot of 1 M citric acid buffer
was added to give a concentration of 50 mM citric acid buffer in the sample flasks.
Antimicrobials tetracycline and cyclohexamine were added to make a concentration of 10 mg/L
each to help prevent contamination of hydrolyzate. Next, an enzyme mixture of Cellic CTec2
and HTec2 was added in a protein mass ratio of 2:1, respectively, at an enzyme loading of 30 mg
enzyme/g glucan and the protein contents of the enzymes were based on the Bradford assay.
Samples were then mixed well and placed in a shaking incubator at 50°C with 180 rpm shaking
for seven days. Sugar concentrations in the hydrolysate were determined by HPLC using the
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method described in the NREL / TP 510-42618 protocol and converted to glucose yields based
on the solids content in the reaction vessel and glucan content in the undelignified biomass.
Water Retention Value
Water retention value (WRV) was determined according to a modified version of TAPPI
UM 256 as described in a previous work (Williams & Hodge, 2014). Briefly, the biomass
samples were filter-washed using a Buchner funnel containing a 200 mesh stainless steel screen
at the bottom as the porous base. The solids remaining after pretreatment and delignification
were washed with approximately 700 mL of deionized water and vacuum-filtered to a moisture
content of approximately 80%. Then, ~2.5 g of wet biomass was inserted into a spin-column
(Handee Spin Column Cs4 from Thermo Scientific) modified to have a 200 mesh stainless steel
screen as the membrane directly under the biomass. The spin columns were then centrifuged at
900 x g for 15 min. The drained biomass was then weighed in an aluminum tray and placed in
an oven at 105°C for 3 hours, and then weighed again. The WRV is the ratio of the mass of
water remaining in the biomass after centrifuging divided by the mass of dry biomass. Samples
were measured in triplicate and errors bars represent standard deviations.
Differential Scanning Calorimetry
Solid residue after AHP pretreatment was washed with 500 mL of water using a Buchner
funnel with a 200 mesh porous base and drained under vacuum to a moisture content of ~80%.
Approximately 15 mg of wet biomass samples was placed into DSC aluminum pans (TA
Instruments, Part #900786.901 bottom and Part #900779.901 top) and then run on DSC. The
measurement of freezing bound water was performed the same to the method by Park et. al
(Park, et al., 2006). Briefly, sample pans were subjected to a gradient and isothermal melting
regime which started by cooling the pan to -30 oC and holding for 5 min, and then scanning at 1
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o

C/min to -20 oC and holding again for 5 min. This procedure was continued in the same way for

the higher temperatures of -15, -10, -6, -4, -2, -1.5, and -1.1 oC which relate to the melting
temperature depressions that correspond to pore diameters 2.6, 4.0, 6.6, 9.9, 19.8, 26.4, and 36.0
nm respectively (Brun, et al., 1977; Park, et al., 2006). The size of the pore is related to the
depressed melting temperature by the equation:

Where T0 is the 0 oC, σk is the surface tension between water and ice, ωk is the specific
volume of ice, ΔHs is the specific heat of fusion (1000 kg/m3), and d is the pore diameter
(Burghoff & Pusch, 1979). The amount of water in each pore size was determined by integrating
the area of each peak in the thermogram and dividing that area by the specific heat of fusion for
water (334 J/g) which gives the mass of water per mass of slurry in each pore size. Figure 30
shows an example of the DSC thermograms, where the negative heat flow indicates heat being
absorbed by the sample, and each peak is an endotherm.

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Figure 30: Example of a thermogram obtained from DSC at the different melting temperatures.
The temperature regime with the heat-up and isothermal holds is shown in blue and the heat flow
shown in green

Results and Discussion
AHP delignification has been shown to impact cell wall composition by removing xylan
and lignin (chapter 1) and to increase cell wall/water affinity by increasing carboxylate content
and likely porosity (chapter 3). The use of DSC to quantify pore size changes following AHP
delignification confirms that porosity does in fact increase. Figure 31 shows the cumulative
bound water (freezing water) versus the pore diameter for corn stover (A) and switchgrass (B)
for the four different conditions of AHP delignification. There is a clear increase in bound water
in the larger pore sizes quantified for the more severe AHP delignification conditions, indicative
of increased porosity. However, the increased porosity is relatively small between the more
severe conditions of AHP delignification, for example the 12% and 25% conditions for corn
stover and the 6%, 12% and 25% conditions for switchgrass are very similar. In fact the average

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pore diameter for the alkali, 6%, 12% and 25% AHP conditions are 5.9, 9.9, 12.7 and 14.6 for
corn stover and 3.7, 12.2, 12.7, and 14.7 for switchgrass, respectively. However, the
digestibilities of these different materials are notably different indicating that porosity itself is
not solely responsible for differences in digestibility. Higher depression temperatures and thus
pore diameters were investigated, however, endotherm peaks became erratic after temperatures
of -0.5 o C. This is noteworthy because it has been suggested that there could be a significant
amount of pores larger than even 400 nm (Park, et al., 2006). Furthermore, freezing bound water
content has also been shown to increase with decreasing crystallinity (Nakamura, et al., 1981),
which may contribute to the difficulty quantifying changes at the higher temperatures. However,
this limitation may also be due to the sensitivity of the equipment or the characteristics of the
pores larger than 100 nm themselves. The non-freezing bound water content was not determined
for this work, but it has been shown to be constant even when the freezing bound water content
is increased by making material more absorbent (Ping, et al., 2001).

Figure 31: Cumulative freezing bound water content as a function of pore diameter for AHP
delignified corn stover (A) and switchgrass (B) obtained from DSC melting temperature
depression endotherms
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Similar to the results shown in chapter 3, Figure 32 shows the correlation between
glucose yield and WRV for AHP delignified corn stover and switchgrass (A) and LHW and
AFEX pretreated switchgrass combined with AHP delignification (B); data are shown on two
separate graphs to highlight the deviations from linearity for some pretreatment combinations.
Figure 32A shows there is a clear linear correlation, R2=0.951, and all data are within a 90%
confidence interval (dashed lines) when both corn stover and switchgrass data points are
regressed together. This is in agreement with results previously published by the author
(Williams & Hodge, 2014). Figure 32B shows the linear model with confidence interval from
Figure 32A again, and the results of combined LHW and AFEX pretreatment with AHP
delignification. The AHP only, low intensity LHW (160 oC, 5 min) and one of the mid intensity
LHW (160 oC, 1 h) results fall within the 90% confidence interval. However, the high intensity
LHW (190 oC, 5 min) and the AFEX samples deviate significantly from the simple linear model.
The region of biomass properties that contribute to increased WRV and digestibility
improvement no longer overlap for these materials, at least in the same way as the other samples.
A potential reason for this deviation may be explained by the composition changes that take
place during the different pretreatment types. Figure 33 shows the relationship between xylan
and glucan contents for switchgrass when performing AHP delignification, LHW pretreatment,
and AFEX pretreatment with increasing intensity (for AHP this means increasing H 2O2
concentration, and for LHW and AFEX, it means increasing temperature). AHP delignification
causes an increase in xylan and glucan contents with increasing intensity due to oxidation and
solubilization of lignin. Xylan, being an amorphous polysaccharide, contributes significantly to
water sorption (Weber, et al., 1993), and therefore, higher xylan containing material would be
expected to have higher WRV. Also, with higher glucan content, and therefore glucan
138

accessibility, digestibility increases as well. LHW pretreatment primarily removes or
redistributes xylan, and redistributes lignin on cell wall surfaces (Holopainen-Mantila et al.,
2013). Therefore, the WRV would be expected to decrease with xylan loss, but as lignin is
redistributed and becomes less occlusive to enzyme access to cellulose, and because of an
increase in glucan content and accessibility, digestibility will still increase. The same trend is
observed following AFEX pretreatment as well; there is a decrease in xylan content, as xylan is
more soluble once material is added to water for hydrolysis, and there is an increase in glucan
content. This may explain why there are differences in WRV and hydrolysis yield correlations
between these different pretreatment types. However, this does not necessarily mean that for
LHW and AFEX pretreatments there is not a linear correlation between glucose yield and WRV.

Figure 32: Correlation of glucose yield and WRV for (A) AHP delignified corn stover and
switchgrass with linear regression model and 90% confidence interval shown in dashed lines and
(B) the linear model from graph A and data from combined LHW and AFEX pretreatments with
AHP delignification

139

30%

Xylan Content

Arrows Indicate
Increaseing
Pretreatment Intensity

20%
Untreated
Switchgrass

10%

Switchgrass
AHP
LHW

AFEX
0%
10%

20%

30%
40%
Glucan Content

50%

60%

Figure 33: Relationship between xylan and glucan content changes for AHP delignification,
AFEX, and LHW pretreatment of switchrass (arrows indicate increasing pretreatment type
intensity, which is temperature for AFEX and LHW, and H 2 O2 loading for AHP)

Although the linear correlation did not hold true for all the different pretreatment types
and conditions regressed together, WRV and glucose yield may still be linearly correlated on an
individual pretreatment technology basis. Figure 34 shows the correlations between glucose
yield and WRV for the AHP delignified corn stover and switchgrass shown in Figure 32A and
also AFEX pretreated corn stover and switchgrass pretreated under two different conditions of
ammonia loading (1.5 and 2 g/g) and four different temperatures (60, 90, 120, and 150 oC) for
each ammonia condition. It is interesting to note that glucose yield for both AFEX pretreatment
conditions are clearly linearly correlated with WRV but have different slopes, and different
slopes from that of the AHP delignified material. This indicates that the respective pretreatments

140

do impact digestibility and WRV in a way that relates the two variables, but that the relationship
is not universally correlated across the different pretreated biomass properties.

Figure 34: Correlation between glucose yield and WRV for AHP delignified corn stover and
switchgrass (presented in Figure 32A) and AFEX pretreated corn stover and switchgrass at two
different ammonia loadings , 1.5 and 2 g/g biomass, and a range of temperatures, 60, 90, 120,
and 150 oC

The simple linear model relating glucose yield and WRV was not sufficient to explain all
the data across multiple feedstocks, pretreatment technologies and conditions. It may be
worthwhile to expand the linear model to include other independent predictor variables by
performing multiple linear regressions (MLR). Figure 35 shows a heat map of the correlation
matrix for the response variable glucose yield, and the predictor variables WRV, glucan content,
xylan content, and lignin content. Glucan and xylan are both noticeably correlated to glucose
yield in addition to WRV, albeit not as strongly. While lignin content is not correlated to
141

glucose yield, but is to xylan content. Therefore, MLR models will be investigated using
combinations of the variable presented in Figure 35 to find a model capable of sufficiently fitting
all the data presented so far.

L

X

Var2

value
1.0
0.5
G

0.0
-0.5
-1.0

WRV

Y

Y

WRV

G

X

L

Var1

Figure 35: Heat map of correlation matrix for the response variable glucose yield (Y), and four
predictor variables, WRV, glucan (G), xylan (X) and lignin (L) content

Model selection using the statistical selection criteria stepwise AIC, R2, adjR2, and
Mallow’s Cp are shown in the appendix of this chapter, and the best model is presented here.
142

Chapter 3 presented a simple linear model using WRV as the sole predictor variable, and that
will be shown here as Model 1. The best fitting model using all the data presented thus far based
on the selection criteria and model simplicity (least number of variables) is present here as
Model 2, which includes WRV and xylan content after pretreatment, and has an R2=0.900.
Figure 36 shows predicted versus actual glucose yield for these two models. Other models that
used more than two predictors where also analyzed as shown in the appendix to this chapter.
Using WRV, glucan and xylan content or xylan and lignin content did give better models based
on selection criteria. However, upon statistical analysis of these models, the glucan and the
xylan contents were found to not be statistically important based on t and F-tests. The R2 and
adjusted R2 values for all the models investigated here were on the order of 0.8 to 0.9. The fit of
these models are comparable with other models reported in the literature, for example a near IR
based model showed good results in which the range of R2 values for various response models
was between 0.69-0.87 for LHW, dilute acid and alkali pretreated miscanthus samples.
However, the range of composition values and hydrolysis yields was not as broad as those used
here (Huang et al., 2012). Another linear model based on FTIR spectra using partial least
squares (PLS) regression models obtained fits from 0.84-0.99 R2 for alkali pretreated
switchgrass, big bluestem grass, several prairie grasses and corn stover (Sills & Gossett, 2012).
An MLR model using lignin, acetyl, and carbohydrate content and cellulose crystallinity to
predict glucan and xylan conversion for a range of enzyme loading conditions during hydrolysis
and at different hydrolysis times for hybrid poplar subjected to various delignification,
acetylation and decrystallization techniques to give very good predictions with R 2 of 0.95 or
higher (Zhu, O'Dwyer, Chang, Granda, & Holtzapple, 2010). More complex models using
artificial neural network approximations of non-linear functions for composition based variables

143

have also been shown to predict enzymatic hydrolysis yields very well (O'Dwyer, Zhu, Granda,
Chang, & Holtzapple, 2008). Non-linear composition based models using lignin and glucan
content have also been investigated with good results but for a limited range of composition
values (Kim & Holtzapple, 2006).
Therefore, the MLR model presented here which uses WRV and composition information
has similar utility to those presented in the literature, and may have better predictive power over
a wider range of biomass composition properties due to the utilization of WRV. Furthermore,
the model performed well for three different pretreatment technologies, and combinations of
these pretreatments, over a range of pretreatment conditions. Much of the models found in the
literature were limited to using one pretreatment type over a more narrow range of conditions.

Predicted Glucose Yield (%)

100

adjusted R2=0.625

80

adjusted R2=0.900

60
Model 1
40

Model 2

20

0
0

20

40
60
80
Actual Glucose Yield (%)

100

Figure 36: Predicted versus actual glucose yield for two linear regression models: model 1
includes WRV only, while model 2 includes WRV and residual xylan content after pretreatment

144

Conclusions
AHP delignification of corn stover and switchgrass did increase the average pore size of
biomass fibers evidenced by DSC pore size distribution analysis. This corroborates with
increased WRV and increased glucose yield which would be expected with increased porosity.
WRV did linearly correlate well for AHP delignified and low to mild LHW pretreated corn
stover and switchgrass samples as observed previously (Williams & Hodge, 2014). However,
the linear correlation was not observed to more severe LHW pretreatment conditions and AFEX
pretreated switchgrass. This is likely due to the relative changes in composition between lignin,
xylan and cellulose that contribute to water sorption and enzyme accessibility differently. A
single variable linear model, using WRV as the predictor, was not sufficient to predict glucose
yield within a statistically acceptable range, R2=0.65, for all the pretreatment types and
conditions used in this work. It was found however, that AFEX pretreatment does yield material
that has a compelling linear correlation between glucose yield and WRV, albeit with a different
slope as that of AHP pretreated material. Therefore, by including other variables, in this case
xylan content, in addition to WRV in an MLR model, the predictive power of this new multiple
linear model is improved significantly, R2=0.90.

145

APPENDIX

146

Appendix: Model Selection Using Multiple Linear Regression (MLR)
The determination of a model(s) with the best fit was done using statistical selection
criteria including adjusted R2,Mallow’s Cp, and Akaike information criteria (AIC) both forward
and backward step-wise. Following is a description of the analysis process for choosing the best
model, which is described as Model 2 in the chapter above. Table 9 below lists all of the
variables used in this analysis with the coded symbol used in most of the software outputs.
Model selection begins with the full model including all four predictor variables for glucose
conversion.
Table 9: Variables with coded symbols and variable descriptions
Variable
G Conv
WRV
Glucan
Xylan
Lignin

Symbol
Y
WRV
G
X
L

Description
The percent of cellulose converted to glucose
Water retention value
The percent glucan in residual solids
The percent xylan in residual solids
The percent lignin is residual solids

Analysis of Full Model
Figure 37 below shows the scatter plot matrix, residuals of the full model and the
correlation matrix for the full model

147

Simple Scatterplot Matrix
2.5

3.5

5

15

25

60

100

1.5

2.5

3.5

20

Y

55

1.5

WRV

15

25

35

45

G

30

5

X

10

20

L
20

60

100

35

45

55

10

20

30

Figure 37: Scatter plot matrix for the respone variable (Y, glucose yield) and all four predictor
variables

148

18
34

40

60

80

1.0
20

40

60

80

100

Residuals vs Leverage

-1

0

1

2

-1

0

1

0.5

21

-2

1
0
-1
34

2

Normal Q-Q
Standardized residuals

Fitted values

2

Fitted values

18

-2

11

0.5

100

11

-2

Standardized residuals

20

34
18

0.0

0
-20

Residuals

10

11

1.5

Scale-Location
Standardized residuals

20

Residuals vs Fitted

0.5

18

Cook's distance
34
0.00

Theoretical Quantiles

0.10

0.20

1

0.30

Leverage

Figure 38: Residual vs Fitted plots, Normal plot and Leverage plot of all data

From the scatter plot matrix and multivariate correlations, we can see that the response is
linearly associated with all of the predictors. So the first-order regression model is a good fit
based on all predictor variables. Fitting the full model gives:

or
149

The plot of residuals in Figure 38 doesn’t show apparent evidence against normality,
although there is noticeable tailing in the Normal Q-Q plot. The scatter plot matrix does show
multicolinearity of the predictors, for instance, there are strong linearity between xylan and
lignin.
Sub-model selection
The next step is to find some subset of predictors which is adequate to fit the response
variable. Three optimal models were chosen for statistical investigation shown below, models A,
B, C, and D. Model A is the full model including all four predictor variables. This model is best
based on the four selection criteria investigated, AIC, Mallow’s Cp, R 2, and adjR2 all shown in
Table 10. However, simpler models that only include less than all the predictors were
considered as well. WRV was used in all models. Model B additionally uses glucan and xylan
content, with the idea that xylan contributes more significantly to water affinity than the other
components, and glucan content is an indicator of enzyme accessibility as described in the
chapter above. Model C uses xylan content and lignin content in addition to WRV. And Model
D uses only WRV and xylan content for reasons that will described below.
Model A:
Model B:
Model C:
Model D:

150

Comparison of all four models using AIC, Mallow’s Cp, adjusted R 2 , and R2 , is shown in
Table 10 below. Model A, the full model, has the best values for all criteria shown. However,
Models B, C, and D are not much behind the full model. It is reasonable then to desire to select
any of these models which are simpiler in that they have fewer predictor variables, and the
simplest is Model D which only contains the composition component X. To test whether G and
L can be dropped from Models C and D, we determine if the coefficients

= 0. The p-

values of the t test and F test are 0.36 and 0.07 for models B and C respectively (Table 11) which
are both >0.05, so the hypothesis that

cannot be rejected. So it is reasonable to

drop the predictor variables G and L from Models B and C, indicating that Model D is the best
choice based on the selection criteria shown and for simplicity purposes.
Table 10: Selection criteria values for the four models being investigated
Model
Full
WRV, G, X
WRV, X, L
WRV, X

AIC

Cp

162.4
166.9
164.2
165.9

5
9.1
6.4
8.1

151

R2
0.8533
0.8223
0.8362
0.8175

adjR2
0.8331
0.8046
0.8199
0.8055

Table 11: Summary of t and F test for the inclusion of G and L in best model selection
t-test for Model B

F-test for Model C

Therefore, the final estimated linear regression function for Model D is

This model, Model D, is presented as Model 2 in the text of the chapter above.

152

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157

Chapter 5 - Summary of Work, Conclusions, and Future Directions
The contents of this dissertation have included practical explorations of potential process
schemes and process conditions for the utilization of lignocellulosic carbohydrates for biofuel
production by AHP pretreatment of grass feedstocks and alkali pretreatment integration with
sorghum sugar extraction. Furthermore, this work also includes fundamental investigations of
the impact of pretreatment on water/biomass interaction properties and their utility for
understanding cell wall deconstruction with enzymes. In chapter 1, it was shown that AHP
pretreatment delignifies and solubilizes a large fraction of lignin and xylan, and that they are
removed concurrently, while leaving cellulose in tact. The effectiveness of the pretreatment is
optimal at pH 11.5 and that effectiveness can be improved simply by performing pretreatment at
higher solids loading, and concequently at higher temperatures. Inhibitor formation was also
determined to be minimal for the feedstocks investigate here. However, to achieve relatively
high digestibility improvements requires H 2O2 at quantities that make the process cost
unattractive. Future work will require strategies that reduce H 2O2 loadings while still achieving
high pretreatment effectiveness. Work in this area has been performed by colleagues using a
catalytic approach where metal based catalysts improve H 2O2 utilization, by reducing wasteful
degradation reactions of the reactive oxidants, with promising successon woody biomass
feedstocks (Li, Chen, Hegg, & Hodge, 2013). Another strategy is to use AHP as a delignifying
post-treatment in conjunction with other pretreatment techniques such as alkali pretreatment (Liu
et al., 2014). It is likely that whatever approach is taken to utilize AHP pretreatment will be
feedstock specific, targeting biomass that contains a lot of alkali labile constituents and/or
oxidatively susceptible lignin.

158

In chapter 2 it was shown that soluble sugar-rich biomass feedstock such as sweet
sorghum, can incorporate existing technologies for soluble sugar extraction with lignocelluloses
pretreatment to generate dirty sugar streams that are very fermentable. A combined alkali
pretreatment and diffuser type soluble sugar extraction followed by separate bagasse hydrolysis
utilized ~70% of the total sugars in a sweet sorghum, including both soluble and structural
carbohydrates, for fermentation to ethanol at an 85% yield. Furtur work could investigate this
type of approach on other sorghum types, for example high starch containing sorghums could
perform extraction with amylase washes, or more alkali labile sorghums could use the technique
presented here.
Chapters 3 and 4 demonstrated that biomass properties that contribute to water/biomass
interactions such as swelling can be quantified and correlated to hydrolysis yield. WRV
represents a useful metric for doing this, and a simple linear regression model can be developed
to correlate with glucose yield for a sizable range of biomass properties. This range of
properties, as well as pretreatment techniques and conditions, can be expanded and a MLR
model which includes WRV and compostion information, in this case xylan content, can be used
to predict glucose yield with positive results. This type of predictive model is simpler than many
of the models presented in the literature and may be a useful screening tool for identifying
biomass feedstocks that would be particularly well suited for effective cell wall deconstruction.

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