Water retention value predicts biomass recalcitrance for pretreated lignocellulosic materials across feedstocks and pretreatment methods

Understanding the causes of lignocellulosic biomass recalcitrance is necessary for developing robust biomass conversion processes for fuels and chemicals. A key factor in biomass recalcitrance is the physical and chemical relationship between biomass and water. Water is known to be important for enz...

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Bibliographic Details
Published inCellulose (London) Vol. 25; no. 6; pp. 3423 - 3434
Main Authors Weiss, Noah D., Felby, Claus, Thygesen, Lisbeth G.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.06.2018
Springer Nature B.V
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Summary:Understanding the causes of lignocellulosic biomass recalcitrance is necessary for developing robust biomass conversion processes for fuels and chemicals. A key factor in biomass recalcitrance is the physical and chemical relationship between biomass and water. Water is known to be important for enzymatic hydrolysis both because it is a co-substrate for cellulose hydrolysis, but also because it acts as a swelling agent that allows enzymes access to the substrate. It has been shown that the water retention value, and water constraint as measured by spin–spin low field NMR (T 2 LFNMR) techniques, correlated to biomass recalcitrance for similar lignocellulosic materials pretreated at different severities. In this work, water retention and water constraint was measured across species and pretreatment methods and compared to the enzymatic digestibility of the cellulose fraction. There is an overall positive correlation between the water retention value and glucose hydrolysis yields. Average water constraint in the samples (as represented by monocomponent T 2 decay times) could not be correlated to biomass recalcitrance; however a relationship was found between the relative amount of more highly constrained water measured and hydrolysis performance. Feedstock heterogeneity and differences in sample morphology may account for the variation in the sample set. Further research is needed to develop these predictive methods, but can be applied with good accuracy on specific feedstock types or for specific pretreatment methods.
ISSN:0969-0239
1572-882X
DOI:10.1007/s10570-018-1798-z