Evidence map›Paper›PMID 41697503›Full record

ArticleApplied biochemistry and biotechnology2026

Deep Eutectic Solvent Pretreatment to Enhance the Enzymatic Saccharification Efficiency of Cotton Fibers.

Shiqi Luo, Yuzheng Lu, Jiajia Fu

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In one paragraph

Article in Applied biochemistry and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shiqi LuoNational Engineering Research Center of Fiber-based Composites, College of Textile Science and Engineering, Jiangnan University, Wuxi, 214122, China.
Yuzheng LuNational Engineering Research Center of Fiber-based Composites, College of Textile Science and Engineering, Jiangnan University, Wuxi, 214122, China.
Jiajia FuKey Lab of Sustainable Low-carbon Technologies for Textile Dyeing and Finishing, Ministry of Education, Jiangnan University, Wuxi, 214122, China. kathyfjj@126.com.ORCID http://orcid.org/0000-0003-3067-2695

Funding

Jiangsu Provincial Policy Programme-International Cooperation Projects BZ2020010National Natural Science Foundation of China 51703085
6 · The paper itself

Abstract

Cotton fibers constitute one of the most abundant cellulose-rich resources, offering potential as feedstock for bioethanol production through enzyme-catalyzed hydrolysis into fermentable sugars. However, the inherent structural limitations of fibers considerably restrict cellulase accessibility, leading to low saccharification efficiency. To address this limitation, a pretreatment strategy employing a deep eutectic solvent (DES), comprising choline chloride (ChCl) and p-toluenesulfonic acid (TsOH) at a molar ratio of 1:1 and 75% (v/v) concentration, was investigated. Suitable pretreatment conditions, 90 ℃ for 1 h with a solid-liquid ratio of 1:30, effectively enhanced the yield of enzyme-catalyzed hydrolysis by approximately 1.8 times. Additionally, the recyclability assessment indicated sustained pretreatment efficacy over at least three cycles of DES. Moreover, a notable synergistic effect was observed when cellulase and cellobiase were compounded at a volume ratio of 4:1, which further enhanced the yield by around 1.4 times. The integration of DES pretreatment with compound enzyme system resulted in a remarkable 2.4 times increase in saccharification efficiency, achieving within 2 h a hydrolysis yield equivalent to that obtained after 12 h using cellulase alone. This study provides critical insights into efficient and sustainable strategies for enzyme-catalyzed hydrolysis of cotton fibers and demonstrates the promising applicability of DES in cotton biomass valorization.

Indexed as

beta-GlucosidaseCellulaseCotton FiberDeep Eutectic SolventsBenzenesulfonatesCholineHydrolysisSolvents4-toluenesulfonic acidBenzenesulfonatesbeta-GlucosidaseCellulaseCholineDeep Eutectic SolventsSolventsBiocatalysisBiomassCottonDeep eutectic solventEnzyme-catalyzed hydrolysis

Identifiers

PMID41697503

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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.