Exploring the mechanism of high degree of delignification inhibits cellulose conversion efficiency

Ding, Dayong; Zhou, Xia; You, Tingting; Zhang, Xun

doi:10.1016/j.carbpol.2017.11.057

Cited by 26 publications

(7 citation statements)

References 38 publications

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“…Previous work has demonstrated the ability of the Raman peak centered at 1096 cm –1 , which is assigned to the vibration of CC and CO stretching in carbohydrates, to normalize the scattering peak of lignin at 1600 cm –1 . , Moreover, Agarwal (2011) established a linear model based on the relationship between the standardized relative intensity (intensity of peak 1600 cm –1 /intensity of peak 1096 cm –1 ) and the lignin content determined by the WCMs. , However, the relative intensity needs to be further “corrected” by normalizing with the carbohydrate content in order to reduce the impact of scattering contribution at 1096 cm –1 assigned to CC and CO stretching of the carbohydrate . The peak located at approximately 2895 cm –1 in the Raman biomass spectra is generally assigned to the vibration of C–H in CH and CH 2 in cellulose. , This peak was observed to disappear for the Raman spectra of spruce milled wood lignin (MWL). Therefore, the peak of 2895 cm –1 is irrelevant to the lignin content and can be employed to standardize the lignin-related band intensity/area.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Raman spectroscopy can complement NIR and has also been successfully applied to evaluate the lignin content. ,, Unlike NIR, Raman spectroscopy is not sensitive to the sample moisture content as it is a water-insensitive technique . Suitable mathematical models have recently been employed to determine the prediction ratios of syringyl-to-guaiacyl (S/G) units in lignin as well as the classification of lignin. , However, the auto-fluorescence originating from lignin or other chemicals in lignocellulosic materials can lead to a broad signal in the resultant Raman spectrum . Furthermore, the conjugated lignin substructures can initiate the additional scattering contribution of Raman signals, resulting in a deviation from the prediction .…”

Section: Introductionmentioning

confidence: 99%

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Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform–Raman Spectrometry

et al. 2021

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The quick and non-invasive evaluation of lignin from biomass has been the focus of much attention. Several types of spectroscopies, for example, near-infrared (NIR) and Fourier transform–Raman (FT–Raman), have been successfully applied to build quantitative predictive lignin models based on chemometrics. However, due to the effect of sample moisture content and ambient humidity on its signals, NIR spectroscopy requires sophisticated pre-testing preparation. In addition, the current FT–Raman predictive models require large variations in the independent value inputs as restrictions in the corresponding mathematical algorithms prevent the effective biomass screening of suitable genotypes for lignin contents within a narrow range. In order to overcome the limitations associated with the current methods, in this paper, we employed Raman spectra excited using a 1064 nm laser, thus avoiding the impact of water and auto-fluorescence on NIR signals. The optimal baseline correction method, data type, mathematical algorithm, and internal reference were selected in order to build quantitative lignin models based on the data with limited variation. The resulting two predictive models, constructed through lasso and ridge regressions, respectively, proved to be effective in assessing the lignin content of poplar in large-scale breeding and genetic engineering programs.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform–Raman Spectrometry

et al. 2021

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“…While enzymatic digestibility of the neutral steam-pretreated and mechanically refined substrates increased substantially following delignification, this was not the case for the acidic steam-pretreated substrate. This apparent reduction in digestibility following delignification could potentially be attributed to a collapse of cell wall ultrastructure and increased interfibril association of cellulose induced by the high degree of lignin removal in the acidic steam-pretreated substrate, causing reduced accessibility of cellulose toward enzymes and thereby reducing substrate digestibility ( Ding et al, 2018 ). At the same time, the high degree of delignification of the acidic steam-pretreated substrate resulted in a substantial increase in its yield stress and viscosity, likely due to the increased water retention capacity of the delignified substrate, and perhaps also due to the increased strength of interparticle interactions through greater hydrogen bonding at cellulose surfaces unobscured by lignin ( Shao and Li, 2006 ).…”

Section: Discussionmentioning

confidence: 99%

Enzyme-Mediated Lignocellulose Liquefaction Is Highly Substrate-Specific and Influenced by the Substrate Concentration or Rheological Regime

Zwan

Sigg

et al. 2020

Front. Bioeng. Biotechnol.

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The high viscosities/yield stresses of lignocellulose slurries makes their industrial processing a significant challenge. However, little is known regarding the degree to which liquefaction and its enzymatic requirements are specific to a substrate's physicochemical and rheological properties. In the work reported here, the substrateand rheological regime-specificities of liquefaction of various substrates were assessed using real-time in-rheometer viscometry and offline oscillatory rheometry when hydrolyzed by combinations of cellobiohydrolase (Trichoderma reesei Cel7A), endoglucanase (Humicola insolens Cel45A), glycoside hydrolase (GH) family 10 xylanase, and GH family 11 xylanase. In contrast to previous work that has suggested that endoglucanase activity dominates enzymatic liquefaction, all of the enzymes were shown to have at least some liquefaction capacity depending on the substrate and reaction conditions. The contribution of individual enzymes was found to be influenced by the rheological regime; in the concentrated regime, the cellobiohydrolase outperformed the endoglucanase, achieving 2.4-fold higher yield stress reduction over the same timeframe, whereas the endoglucanase performed best in the semi-dilute regime. It was apparent that the significant differences in rheology and liquefaction mechanisms made it difficult to predict the liquefaction capacity of an enzyme or enzyme cocktail at different substrate concentrations.

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“…As mentioned above, the productive adsorption of cellulase is the cellulase adsorbed on cellulose and the non-productive adsorption of cellulase means the cellulase absorbed on lignin [25,29,30]. Thus, the productive adsorption of cellulase is the total adsorption of cellulase for the pure cellulose, Avicel.…”

Section: Effect Of Ls On Cellulase Adsorption On Lignocellulose (Da-scb)mentioning

confidence: 99%

Exploring why sodium lignosulfonate influenced enzymatic hydrolysis efficiency of cellulose from the perspective of substrate–enzyme adsorption

Zheng

Lan

et al. 2020

Biotechnol Biofuels

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Background: Cellulase adsorbed on cellulose is productive and helpful to produce reducing sugars in enzymatic hydrolysis of lignocellulose; however, cellulase adsorbed on lignin is non-productive. Increasing productive adsorption of cellulase on cellulose would be beneficial in improving enzymatic hydrolysis. Adding lignin that was more hydrophilic in hydrolysis system could increase productive adsorption and promote hydrolysis. However, the effect mechanism is still worth exploring further. In this study, lignosulfonate (LS), a type of hydrophilic lignin, was used to study its effect on cellulosic hydrolysis. Results:The effect of LS on the enzymatic hydrolysis of pure cellulose (Avicel) and lignocellulose [dilute acid (DA) treated sugarcane bagasse (SCB)] was investigated by analyzing enzymatic hydrolysis efficiency, productive and nonproductive cellulase adsorptions, zeta potential and particle size distribution of substrates. The result showed that after adding LS, the productive cellulase adsorption on Avicel reduced. Adding LS to Avicel suspension could form the Avicel-LS complexes. The particles were charged more negatively and the average particle size was smaller than Avicel before adding LS. In addition, adding LS to cellulase solution formed the LS-cellulase complexes. For DA-SCB, adding LS decreased the non-productive cellulase adsorption on DA-SCB from 3.92 to 2.99 mg/g lignin and increased the productive adsorption of cellulase on DA-SCB from 2.00 to 3.44 mg/g cellulose. Besides, the addition of LS promoted the formation of LS-lignin complexes and LS-cellulase complexes, and the complexes had more negative charges and smaller average sizes than DA-SCB lignin and cellulase particles before adding LS. Conclusions:In this study, LS inhibited Avicel's hydrolysis, but enhanced DA-SCB's hydrolysis. This stemmed from the fact that LS could bind cellulase and Avicel, and occupied the binding sites of cellulase and Avicel. Thus, a decreased productive adsorption of cellulase on Avicel arose. Regarding DA-SCB, adding LS, which enhanced hydrolysis efficiency of DA-SCB, increased the electrostatic repulsion between DA-SCB lignin and cellulase, and therefore, decreased non-productive adsorption of cellulase on DA-SCB lignin and enhanced productive adsorption of cellulase on DA-SCB cellulose.

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Exploring the mechanism of high degree of delignification inhibits cellulose conversion efficiency

Cited by 26 publications

References 38 publications

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform–Raman Spectrometry

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform–Raman Spectrometry

Enzyme-Mediated Lignocellulose Liquefaction Is Highly Substrate-Specific and Influenced by the Substrate Concentration or Rheological Regime

Exploring why sodium lignosulfonate influenced enzymatic hydrolysis efficiency of cellulose from the perspective of substrate–enzyme adsorption

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