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Elucidating the interactive impacts of substrate-related properties on lignocellulosic biomass digestibility: A sequential analysis Liyuan Chai, Mingren Liu, Xu Yan, Xunqiang Cheng, Tingzheng Zhang, Mengying Si, Xiao-Bo Min, and Yan Shi ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b00592 • Publication Date (Web): 18 Mar 2018 Downloaded from http://pubs.acs.org on March 19, 2018

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Elucidating the interactive impacts of substrate-related properties on

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lignocellulosic biomass digestibility: A sequential analysis

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Liyuan Chai1, 2, Mingren Liu 1, Xu Yan1, 2, Xunqiang Cheng1, Tingzheng Zhang1,

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Mengying Si1, Xiaobo Min1, 2, Yan Shi*1, 2

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1

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School of Metallurgy and Environment, Central South University, Changsha 410083, China

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Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha 410083, China

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*To whom correspondence should be addressed. Y. Shi, E-mail: [email protected]

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(Y. Shi); Fax: +86-0731-88710171; Tel: +86-0731-88830875

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L. Chai, M. Liu, X. Yan, X. Cheng, T. Zhang, M. Si, X. Min, Y. Shi, Mailing address:

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No.932 South Lushan Road, Changsha Hunan 410083, P.R. China

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Key words: Enzymatic hydrolysis; Lignin; Cellulose; NMR spectroscopy;

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Lignocellulose biomass; Physicochemical properties; Statistical analysis

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ABSTRACT

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A lack of insight into interactive effects among substrate-related factors holds

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back the determination of dominant factors in efficient sugar conversion. Herein,

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thirteen factors defining compositional and physicochemical properties of

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lignocellulose pretreated by dilute acid/base and enzymes were analyzed through an

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innovative sequence of correlation analysis, principle component analysis, multiple

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linear regression and multiscale statistical validation. Results showed that the lignin

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content, cellulose content and O/C ratio principally affected enzymatic hydrolysis.

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The dominant role was played by the lignin content due to its major recalcitrance

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providing to biomass and concomitant impacts on surface lignin and porosity

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properties. The structural features of lignin played a less pronounced role with high

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lignin content remained. Besides, the sequential analysis revealed different inhibition

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mechanisms for glucan and carbohydrate conversion, i.e., non-productive binding of

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enzymes and steric hindrance of lignin, respectively. The established weighing order

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of interactive factors enlightens more efficient pretreatment strategies.

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INTRODUCTION

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The efficient bioconversion of lignocellulosic carbohydrates into sugars is

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essential for the valorization of lignocellulose. 1 To overcome the innate recalcitrance

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of lignocellulose, different pretreatment strategies have been developed to open the

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substrate structure for cellulose degradation.2-3 The efforts to elucidate their

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underlying mechanisms have been made by investigating the concomitant changes in

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substrate-related factors, such as biomass porosity, lignin/hemicellulose distribution,

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contents and structural features as well as cellulose crystallinity and accessibility.2-6

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Unfortunately, the interactive changes in the biomass during pretreatments often lead

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to conflicting trends in weighting the dominant factors.1-5,

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influencing mechanisms of these factors thus presents a significant challenge and the

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development of a strategy that optimizes biomass digestibility remains hindered.

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Interpreting the

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The interactions among these substrate factors are mainly affected by the

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distribution, content and composition of lignin and hemicellulose in the highly

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dynamic lignocellulosic structure.4, 6, 12 According to a popular morphological model,

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highly ordered cellulose elemental fibrils are cross-linked with hemicellulose and

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embedded in a non-cellulosic polysaccharide matrix. The lignin, as the plasticizer, is

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partially bonded with hemicellulose via lignin-carbohydrate complexes.1, 7-8 Therefore,

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the negative role of lignin in lignocellulose digestion has been long recognized.

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Biomass pretreatments that reduce the lignin content are thought to improve the

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saccharification efficiency.3, 5, 9 Contradicting this view, the sugar release from poplar 3 ACS Paragon Plus Environment

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samples with high syringyl /guaiacyl (S/G) ratio shows less dependency on lignin

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content.9 This study reported a tangled interaction between the lignin structural

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features and the lignin content. The increasing S/G ratio implies less cross-linking in

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the triaxial structure and abates the negative role of the lignin content9, 13 whereas it

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was also reported the S/G ratio contributes relatively little to sugar release.

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Moreover, phenolic compounds of lignin should aggravate the non-productive binding

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of cellulase but their counteraction effect with alcoholic and carboxylic hydroxyls

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remains unclear.4, 15 Consequently, no clear picture of lignin inhibition has emerged.

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Similar obscureness was also displayed on the impacts of hemicellulose. Cellulose

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accessibility proves linearly proportional to hemicellulose removal.3,

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cellulase reaction rate is also linearly related to the pore volume of the biomass3, 8, 10

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but followed by a rapid decline in the conversion rate.16 After the initial hydrolysis of

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hemicellulose and amorphous cellulose, biomass porosity increased17 but the

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concomitant increase in crystalline region and lignin content also hinders the

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enzymatic hydrolysis.18-19 Therefore, all substrate-related factors are mechanically

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important, but the factor playing the dominant role remains unidentified.

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The initial

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The statistical analyses of these arguments have been developed over many years.

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Pairs of linear relationships between factors and biomass digestibility are commonly

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tested by correlation analysis (CA). Unfortunately, CA alone often leads to conflicting

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or confusing trends.3, 5, 9-10, 16-19 In particular, CA is susceptible to database diversity; a

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small sample size or individual data from a specific pretreatment may undermine the 4 ACS Paragon Plus Environment

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accuracy of the correlation results and the reliability of further interpretation. Worse

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still, CA alone cannot handle the tangled interactions among different factors. In some

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studies, statistical analysis has been complemented with principle component analysis

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(PCA) which reduces the dimensionality of the problem.5, 20 However, current studies

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have focused on a limited scale of factors without removing the irrelevant ones.5, 20

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This reduces the robustness of the total variance interpretation and compromises the

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PCA performance. For a more integrated perspective, a database providing diverse

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factors after pretreatments should be combined with a systematic and mechanical

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statistical method.

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In this study, thirteen factors were comprehensively collected after dilute

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acid/base and combined enzyme-chemical pretreatments. These factors included the

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compositional variances, substrate surface properties, cellulose crystallinity, biomass

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porosity and structural features of lignin. The latter pretreatment introduced laccase

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and endo-xylanase for facile bioprocessing and diversified the physicochemical

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changes.21 To identify the dominant factors of biomass digestibility, the interactions

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between the thirteen factors were analyzed through a sequential analysis which

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integrates CA, PCA and stepwise multiple linear regression (MLR).22-23 Analogous to

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impurity removal in cascade filtration, the method excludes the factors that are

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irrelevant or subjected to the collinearity problem, thus guaranteeing that no chance

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correlations or collinearity will undermine the accuracy of the MLR results. To our

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knowledge, our study is the first sequential study on the complex influence of 5 ACS Paragon Plus Environment

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interacting factors on enzymatic hydrolysis. We thus provide a systematic and reliable

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approach for optimizing pretreatment strategies.

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EXPERIMENTAL SECTION

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Pretreatment and enzymatic hydrolysis

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Corn stover ( 0.6, p < 0.05, variance inflation factor < 10 (for evaluating

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the collinearity level) and F-statistic > 15.22 The error in the model during its

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application was then estimated by both internal validation (leave-one-out method,

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LOO; bootstrap method, BOOT) and experimental test (Table S11). The

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cross-validation coefficient Q2 was defined as follows:

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Q2 =1 −

∑ (yi ŷi )2

(2)

2 ∑ (yi y) i

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where yi and ŷi were the detected and predicted glucan/carbohydrate conversion,

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respectively. yi was the average value in dataset. The BOOT was performed on 5000

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randomly sampled subsets. The Q2 and the root mean square error (RMSE) of LOO

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2 and BOOT method were also calculated. A model was considered robust if its QLOO

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2 and QBOOT exceeded 0.5 and RMSE less than 0.3. All statistical analyses were

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performed in SPSS software (Version 21.0, SPSS Inc., USA), the R programming

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language (3.4.1, USA) and MATLAB (2014a, MathWorks, USA).

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RESULTS AND DISCUSSION 10 ACS Paragon Plus Environment

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Correlation analysis of thirteen substrate-related properties with potential

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influence on biomass digestibility

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Thermochemical pretreatment with dilute acid/base as well as facile bioprocessing

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with laccase and xylanase digestion were employed in present study, providing

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diverse changes in the substrate-related factors. 21 For simplicity, we hereafter refer to

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the dilute acid and acid-enzyme pretreatments as acid pretreatment (a similar

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terminology is adopted for base pretreatment; Table S1). Quantile−quantile plots of

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glucan and carbohydrate conversions were performed. All plots were distributed in

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the diagonal line, confirming the validity of a random normal distribution assumption

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(Figure S1).22 Figures 1 and 2, respectively, present the matrix of correlation

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coefficients among factors and the relations between glucan/carbohydrate conversions

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and the thirteen factors. The data falls into three classes: acid pretreated data, base

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pretreated data and untreated data.

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Compositional analysis

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The acid/base pretreatment induced delignification and hemicellulose removal,

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with concomitant changes in the physicochemical factors of the biomass substrate.

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Herein, the lignin content varied from 1.61% to 26.2% and was strongly negatively

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correlated with both glucan (r = −0.830, P < 0.05) and carbohydrate conversions (r =

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−0.866, P < 0.05, Figures 1 and 2a). It’s observed that the glucan and carbohydrate

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conversion of untreated and acid-pretreated samples were less than 50%, whereas 11 ACS Paragon Plus Environment

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those of base-pretreated samples exceeded 50% (Table S2). Lignin forms not only

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hydrophobic networks that restrict the entry of cellulases and hemicellulases into the

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polysaccharide1, 3 but also LCCs that non-productively bind with the enzymes.10, 32

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Residual lignin after pretreatment could further block the progress of cellulase down

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the cellulose chain.33 Therefore, the lignin content exerted a pronounced adverse

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effect on the enzymatic digestibility.

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By contrast, the hemicellulose content was not significantly correlated with glucan

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(r = 0.163, P > 0.05) or carbohydrate conversions (r = 0.151, P > 0.05, Figures 1 and

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2b); this finding is consistent with a previous CA of diversely pretreated samples.20

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Although hemicellulose removal improved the access of cellulases to cellulose, its

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impact on biomass digestibility might be less important. Hemicellulose obstacles are

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frequently overcome by adding accessory enzymes such as xylanase in Cellic®

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CTec2.34 With deacetylation during lignin removal, the inhibition effect of acetyl

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groups on endo-xylanases is also reduced.

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hydrolysis of hemicellulose is thus achieved. Due to the low correlation coefficients,

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the hemicellulose content was excluded from the PCA step in the sequential study.

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A more efficient enzymatic

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The cellulose content was positively correlated with glucan (r = 0.636, P < 0.05)

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and carbohydrate conversion (r = 0.638, P < 0.05, Figures 1 and 2c). Given that the

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acid/base pretreatment only moderately affects the cellulose itself, cellulose content

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mainly varies by concomitant mass reduction during lignin and/or hemicellulose

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removal.

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Specifically, closed regression coefficients of lignin and hemicellulose 12 ACS Paragon Plus Environment

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contents to the increase in cellulose contents were obtained (-0.819 vs. -0.890, Table

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S9). Therefore, the positive relation between cellulose content and glucan and

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carbohydrate conversions resulted from a combined effect of lignin and hemicellulose

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removal.

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Surface O/C ratio

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Besides the lignin in bulk substrate, the surface lignin increases the adhesion

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forces to cellulase, thus aggravating non-productive binding of enzymes.27, 37 Herein,

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the impact of the surface-lignin coverage was identified from the ratio of surface

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oxygen (O1s) to carbon (C1s) obtained by XPS.38 Generally, a higher O/C ratio

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indicates that the reduced-oxygen components (such as lignin, 0.33) are less

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distributed than the oxygen-rich components (such as cellulose, 0.8339).15 For

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confirmation, XPS C1s peaks were deconvoluted. The dominant peak at 284.8 ± 0.1

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eV (C1) was corresponded to the C-C bonds from lignin structure.27 Notably, the O/C

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ratio was negatively correlated with the content of C1 (r = −0.581, P < 0.05, Table S4),

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confirming that lowering surface lignin coverage could increase the surface O/C ratio.

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As presented in Table S4 and Figure 2d, the O/C ratio of lignocellulosic biomass

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varied from 0.371 to 0.658 and was positively correlated with both glucan (r = 0.783,

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P < 0.05) and carbohydrate conversion (r = 0.742, P < 0.05), revealing that lower

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surface lignin coverage contributed to biomass enzymatic digestibility. Indeed, a low

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surface lignin coverage was highly correlated with lignin content (r= −0.666, P
0.05, respectively; Figures 1 and 2e). In fact, cellulose

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crystallinity affected by pretreatment is often less straightforward to enzymatic

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hydrolysis in a complicated biomass structure.2, 40-41 The good correlation between CrI

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and enzymatic digestibility is often obtained from nearly pure cellulose.42 Therefore,

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some studies have associated the CrI with actual cellulose content and constructed a

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new parameter (CrI/cellulose) to assess the actual changes in crystallinity.2,

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Interestingly, the untreated biomass possessed the lowest CrI value but the highest

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CrI/cellulose (1.411, Table S3) and the Cr/cellulose values were lower in the

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base-pretreated samples. The base pretreatment led to cellulose swelling which

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decreased the crystallinity and partially transformed the recalcitrant cellulose Iα to

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amorphous-like cellulose IIII, thus boosting the enzymatic hydrolysis.5,

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suggests that the total crystallinity is mainly increased with the increase in the

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cellulose content, although the crystallinity of the cellulose itself deceases.44-45

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Correspondingly, the correlation between the CrI/cellulose and glucan/carbohydrate

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conversions was improved (r = −0.548/−0.483, P < 0.05, Figures 1 and 2f). In the

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next stage of PCA, CrI/cellulose was preserved as the glucan-conversion-relevant

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factor.

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Biomass Porosity

7, 43

This

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Considering that hydrolysis requires intimate contact between the enzymes and a

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valid polysaccharide surface, biomass porosity should be considered.7 Herein, the

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biomass porosity was estimated from the specific surface area (SSA), pore volume

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(PV) and average pore sizes (APS) determined by nitrogen adsorption. Unexpectedly,

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the glucan and carbohydrate conversions were poorly correlated with SSA (r =−0.218

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and −0.268, P > 0.05, respectively; Figures 1 and 2g) and with PV (r = −0.270 and

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−0.302, P > 0.05, respectively; Figures 1 and 2h). We then separately analyzed the

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acid and base pretreatment data and yielded very different results. In acid pretreatment,

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the porosity of biomass substrate is significantly promoted by hemicellulose removal

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and lignin rearrangement.3 In present study, the acid pretreatment increased the SSA

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from 0.623 m2 g-1 (untreated) to 3.41–7.72 m2 g-1, and the PV from 0.00242 m3 g-1 to

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0.0147–0.0280 m3 g-1 (Figure 2g, h). The larger SSA and PV indicates a more porous

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structure, facilitating enzyme access to the embedded cellulosic microfibrils.2,

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Therefore, the glucan and carbohydrate conversions were strongly positively

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correlated with SSA (r = 0.861 and 0.754, P < 0.05, respectively) and with PV (r =

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0.848 and 0.814, P < 0.05, respectively). However, the base-pretreated biomass was

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less porous (with SSA and PV ranging from 1.22 to 1.97 m2 g-1 and from 0.00335 to

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0.00715 m3 g-1, respectively; Figure 2g, h). The effect of delignification on cellulose

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accessibility is often limited.3, 46 By partially reducing the mechanical support of the

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whole biomass, delignification might induce the partial aggregation of adjacent

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cellulose microfibrils.2,

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between glucan conversion/carbohydrate conversion and SSA (r = −0.101/−0.107, P >

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0.05) and PV (r = −0.129/−0.135, P > 0.05) after the base pretreatment. Notably,

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although polysaccharide digestion increases the structural porosity, the enzymatic

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hydrolysis rate still drops.45, 46 As enzymatic hydrolysis slightly increases the CrI,

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crystalline regions of cellulose cannot tell the whole story.42, 48 Therefore, in addition

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to enzyme factors (including traffic jams),49 the subsequently increased lignin content

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might play more important roles. This suggests that biomass porosity does not play

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the dominant role.17, 19, 46, 50

47

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Corroborating this view, we observed poor correlations

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The APS is also considered as a positive factor for enzyme invasion.32 However,

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the glucan and carbohydrate conversions were significantly negatively correlated with

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APS (r = −0.634 and −0.561, P < 0.05, respectively; Figures 1 and 2i). The APS 16 ACS Paragon Plus Environment

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decreased with the increase of the severity in pretreatments, while adverse trends were

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observed in both SSA and PV. This indicated more nano-pores (>10nm in present

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study, consistent with the reported pore size to enable essential accessibility of

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cellulase protein molecule40, 51) was formed during the ‘peeling away’ of the cell wall

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components in pretreatments.3, 52 At the most fundamental level, these nano-pores

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provides more reaction volumes for the synergistical enzymes, thus facilitating

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enzymatic hydrolysis.53 Consequently, in the next stage of PCA, the APS was

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preserved in the sequential study, whereas the SSA and PV were excluded.

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Lignin structural features

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S/G ratio. Lignin subunits were identified by 2D-HSQC spectra. The S/G ratio varied

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from 0.59 to 6.39 (Figure 2j; for relative contents of syringyl (S), guaiacy (G) and

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p-hydroxyphenyl (H) units, see Table S5). The S/G ratio was positively correlated

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with the glucan and carbohydrate conversions (r = 0.651 and 0.712, P < 0.05,

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respectively; Figures 1 and 2j). This result revealed that a higher S/G ratio facilitated

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sugar release, consistent with the trend observed in a large-scale poplar study.9 Due to

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the open C-5 position, G units not only feature higher affinity to enzymes than S units

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and thus aggravate the non-productive binding of cellulases.11 They could further lead

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to more stable 5-5 and β-5 linkages and form a more cross-linked and recalcitrant

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structure of lignin.13, 54 By contrast, the methoxylated and blocked C-5 position in S

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units preferentially forms β-β linkages, which contributes to the linearity of lignin.9, 55

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Considering G-rich lignin with lower reactivity is also related to less labile β-O-49,

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the relative content of lignin linkages was investigated. The S/G ratio was positively

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correlated with the relative content of β-O-4 (r =0.458, P < 0.05, Table S5), but

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negatively correlated with the relative content of β-5 (r = −0.413, P < 0.05, Table S5).

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These results confirmed the negative role of G-rich lignin.

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The negative influence of lignin content reportedly decreases at higher S/G ratios

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(≥2.0).9 However, the interaction effect between the lignin content and S/G ratio

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remains nonquantitative. Therefore, the relative contribution of these two factors in

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biomass digestibility was additionally weighed by standardised MLR, wherein the

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coefficients of the lignin content were much higher than those of the S/G ratio (glucan

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conversion regression: −0.679 vs. 0.263, carbohydrate conversion regression: 0.681 vs.

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0.323, Table S9). These results implied that enzymatic digestibility might be affected

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by both the lignin content and composition in untreated biomass, but only dominated

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by lignin content in pretreated biomass.56 Regardless of the S/G ratio effect, lowering

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lignin content is preponderant in improving the digestibility.11

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Hydroxyl Features. The non-productive binding of cellulase is often relevant to the

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hydroxyl groups of lignin8, which could be quantified by

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(Figure S3, Table S6). Among the phenolic hydroxyls (PhOH), we detected C5

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substituted condensed phenolic OH, guaiacyl phenolic OH, catechol type OH and

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p-Hydroxyl-phenyl-OH.69 Aliphatic (AOH) and carboxylic acid hydroxyls (COH)

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were summed as a single factor ACOH (aliphatic + carboxylic acid hydroxyls). 4, 57-58 18 ACS Paragon Plus Environment

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P NMR spectroscopy

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The PhOH and ACOH were then summed to give the TOH. Interestingly, the PhOH

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was significantly negatively correlated with carbohydrate conversion (r = −0.509, P
0.05, Figures 1

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and 2 m). PhOH in residual lignin could interact with the amide groups in enzymes

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through hydrogen bond and strengthen the hydrophobic interactions between aromatic

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amino acids in enzymes and the aromatic rings of PhOH. Thus, phenolic compounds

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became disruptive in enzymatic functions.28 This result confirmed the negative role of

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PhOH in cellulase inactivation. However, given the abovementioned structural model

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of lignocellulose (in which lignin is directly linked to hemicellulose), PhOH should

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less strikingly affect the glucan conversion59 and was thus excluded from the

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glucan-conversion-relevant factors in the following PCA stage.

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Unexpectedly, glucan/carbohydrate conversion was negatively correlated with

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ACOH (r = −0.500/−0.580, P < 0.05, Figure 2k) and TOH (r =−0.509/−0.632, P
15). Therefore, it was highly unlikely that the results were observed under the

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null hypothesis.22 All variance inflation factors (VIF) were below 10.0, indicating

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insignificant correlations among the factors. These results guaranteed reliability of the

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regression. In the next internal validation by LOO and BOOT, the cross-validation

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2 2 coefficients were relatively high (QLOO-Glucan = 0.672 and QLOO-Carbohydrate = 0.654). The

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2 BOOT step also yielded sufficiently high coefficients ( QBOOT-Glucan = 0.692 and

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2 QBOOT-Carbohydrate = 0.698). Their RMSELOO-Glucan, RMSELOO-Carbohydrate, RMSEBOOT-Glucan ,

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RMSEBOOT-Carbohydrate were 0.109, 0.195, 0.096, 0.171, respectively. The validation

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coefficients (> 0.5) and RMSEs (< 0.3) confirmed the robustness of the regressions.

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The experimental validation involved in both mild and severe pretreatments was

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further conducted on four additionally-tested samples (Table S11). The errors between

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the predicted and measured sugar conversions were less than 10% and their

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2 cross-validation coefficient QExt also exceeded 0.5 (Table S11), confirming the

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validity of the statistical analysis.

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Eqs. I1 and I2 with sufficient predictability (R2 = 0.784 and 0.832 for glucan and

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carbohydrate conversion, respectively) can be highlighted for promoting enzymatic

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digestibility by delignification. However, the major inhibition mechanism of lignin

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might differ in glucan and carbohydrate conversions. The glucan conversion was

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mainly affected by O/C ratio and lignin content, indicating that its underlying

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mechanism involves surface-lignin coverage. Specially, during acid pretreatment,

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lignin migrates to the biomass surface and aggregates into droplets with a low O/C

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ratio,61 while the largely dissolved surface lignin in base pretreatment increases the

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O/C ratio and improves the biomass digestibility.27 Given that a significantly

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heterogeneous and hydrophobic surface with high lignin content impedes the glucan

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conversion, we inferred that lignin negatively impacts the glucan conversion mainly

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through the non-productive binding of cellulase.

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conversion was mainly influenced by cellulose content besides lignin content. Given

62-63

By contrast, the carbohydrate

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that cellulose is minimally depolymerised in acid/base pretreatment, the cellulose

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content is indirectly varied by both lignin and hemicellulose removal. The removal of

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the recalcitrant components unblocked the initial availability of the biomass substrate.

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This also implied that, for carbohydrate conversion, the steric hindrance might be the

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major negative factor. To avoid these two inhibitors, removing lignin to a low content

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should be the most direct and efficient approach to optimise the amenability of the

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biomass to enzymatic hydrolysis.

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CONCLUSION

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In this work, the substrate-related factors of corn stover pretreated by acid/base

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and enzymes were comprehensively characterised. A statistically-designed sequence

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was employed to account for the interaction effects of multivariate and multiscale

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factors involved in biomass recalcitrance. Among thirteen factors, lignin content,

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CrI/cellulose, APS, TOH, ACOH, PhOH were significantly negative factors, whereas

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cellulose content, O/C ratio and S/G ratio played the positive roles. The hemicellulose

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content, CrI, SSA, and PV proved irrelevant to biomass digestibility. Three

478

representative factors (i.e., lignin content, O/C ratio and cellulose content) were then

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selected by PCA. Finally, a standardised model was constructed by stepwise MLR,

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which mechanically interpreted the impacts of these three factors. The equations were

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determined as Glucan Conversion = 0.225 − 1.38 × Lignin Content + 0.911 × O/C

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ratio and Carbohydrate Conversion = 0.261 − 3.142 × Lignin Content + 1.488 ×

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Cellulose Content. Lignin content was systematically and quantitatively proved most 24 ACS Paragon Plus Environment

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ACS Sustainable Chemistry & Engineering

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dominant. The influence of the lignin structural features (S/G ratios and hydroxyl

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groups) were important but less pronounced when high lignin content remained. More

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importantly, the sequential analysis revealed different inhibition mechanisms for

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glucan and carbohydrate conversions, i.e., non-productive binding of cellulase to

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lignin and steric hindrance of lignin, respectively. Further sequential analysis on

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larger-scale samples would provide deeper insights into biomass digestibility, thus

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enabling the optimisation of the bioconversion process.

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ASSOCIATED CONTENT

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Supporting Information

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The Supporting Information is available free of charge on the ACS Publications

494

website. 31

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Compositional analysis and lignin extraction methods; Q-Q plots, XRD and

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NMR spectra (Figures S1-3); Pretreatment details, compositional analysis and

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mass recovery of samples, XPS, CrI and CrI/cellulose, 2D NMR, 31P NMR, PCA

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results, standardized MLR results and experimental test (Tables S1-11, PDF).

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AUTHOR INFORMATIN

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Corresponding Author

501

Yan Shi

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*E-mail: [email protected]; Fax: +86-0731-88710171; Tel: +86-0731-88830875

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Notes

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The authors declare no competing financial interest.

505

ACKNOWLEDGMENTS

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This work was supported by key project of National Natural Science Foundation

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of China (51634010), National Natural Science Foundation of China (31400115,

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51474102), China Postdoctoral Science Foundation (2017M612594).

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TABLES AND FIGURES

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Table 1. Development of regression equations with the factors selected by PCA using

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stepwise MLR method.

512 #

Equation

R2

F

p

VIF

I1

Glucan Conversion =0.225−1.38×LigninContent + 0.911×O/C ratio

0.784

30.8