Evidence map›Paper›PMID 38668842›Full record

ArticleApplied biochemistry and biotechnology2024

Tuning Structural Characteristics of Corn Stover Through Ammonium and Sodium Sulfite (ASS) Pretreatment for Enhanced Enzymatic Hydrolysis.

Xiangxue Chen, Shizhong Yang, Shuiping Ouyang, Xinchuan Yuan, Junlin Song, Shuai Ding, Yuanyuan Sha, Rui Zhai

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Article in Applied biochemistry and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiangxue ChenSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China.
Shizhong YangSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China.
Shuiping OuyangSchool of Advanced Materials Engineering, Jiaxing Nanhu University, 572 South Yuexiu Road, Jiaxing, 314001, China.
Xinchuan YuanSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China.
Junlin SongSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China.
Shuai DingSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China.
Yuanyuan ShaSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China. shayuanyuan@njust.edu.cn.
Rui ZhaiSchool of Environmental and Biological Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Xuanwu District, Nanjing, 210094, Jiangsu Province, China. ruizhai@njust.edu.cn.

Funding

National Key R&D Program of China 2021YFC2101301National Natural Science Foundation 22308164
6 · The paper itself

Abstract

The ammonia fiber expansion (AFEX) pretreatment of lignocellulosic biomass offers a significant advantage in terms of obtaining high glucan conversion, with the added benefit of ammonia being fully recyclable. However, despite the high efficiency of AFEX in pretreating lignocellulose, relatively high enzyme loading is still required for effective cellulose conversions. In this study, we have updated the AFEX pretreatment method; ammonia and sodium sulfite (ASS) can be used to produce a more digestible substrate. The results demonstrate that ASS-pretreated corn stover (CS) yields a higher fermentable sugar yield compared with AFEX pretreatment, even at lower enzyme loadings. Specifically, at an enzyme loading of 12 mg protein/g glucan, ASS-CS achieved 88.8% glucose and 80.6% xylose yield. Characterization analysis reveals that lignin underwent sulfonation during ASS pretreatment. This modification results in a more negative zeta potential for ASS-CS, indicating a reduction in nonproductive adsorption between lignin and cellulase through increased electrostatic repulsion.

Indexed as

CellulaseLigninSulfitesZea maysAmmoniaAmmonium CompoundsBiomassHydrolysisAmmoniaAmmonium CompoundsCellulaseLigninlignocellulosesodium sulfiteSulfitesAmmonium sodium sulfiteEnzymatic hydrolysisLignocellulosic biomassLow enzyme loadingSulfonation

Identifiers

PMID38668842

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.