Chrome Extension
WeChat Mini Program
Use on ChatGLM

Deep Eutectic Solvents for Efficient Fractionation of Lignocellulose to Produce Uncondensed Lignin and High-Quality Cellulose

ACS SUSTAINABLE CHEMISTRY & ENGINEERING(2025)

Univ Wisconsin

Cited 0|Views7
Abstract
Simultaneously inhibiting lignin condensation and cellulose degradation remains a major challenge for achieving holistic valorization of lignocellulose. Here, we developed a deep eutectic solvent (DES), composed of l-cysteine (Cys) and lactic acid (LA), to fractionate both uncondensed lignin and high-quality cellulose from eucalyptus wood by leveraging the unique properties of Cys, i.e., highly nucleophilic groups (-SH) and hydrogen bond acceptor/donor groups (-NH2 and -COOH). The nucleophilic -SH in Cys effectively quenches the benzylic carbocations (C alpha + ions, formed at the benzylic sites of lignin) that lead to lignin condensation. This enables the high yield of uncondensed lignin (81%) with high retention of beta-O-4 bonds (up to 90%). The separated uncondensed lignin is further depolymerized to prepare monophenols in a satisfactory 43% yield, equivalent to 73% of the theoretical yield. Moreover, the -NH2 and -COOH groups in Cys form extensive hydrogen bonds with the hydroxyl groups in cellulose, thus decreasing the interaction energy of DES on cellulose. As a result, the cellulose achieves an astonishing 99% retention and maintains a high degree of polymerization of 1160. The obtained high-quality cellulose is further conversed into cellulose nanofibers for strong and transparent films. This study provides new insights into the efficient separation of uncondensed lignin and high-quality cellulose from lignocellulose by a novel DES system.
More
Translated text
Key words
lignocellulose fractionation,lignin protection,lignin depolymerization,cellulose nanofibrils
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本研究开发了一种深共晶溶剂(DES),利用其独特的性质有效分离出未缩合的木质素和高质量纤维素,实现木质素的全面增值利用。

方法】:通过将l-半胱氨酸(Cys)和乳酸(LA)结合,制备出一种深共晶溶剂,利用Cys的高度亲核基团(-SH)和氢键受体/供体基团(-NH2和-COOH)特性来抑制木质素缩合和纤维素降解。

实验】:使用l-半胱氨酸和乳酸制备的DES处理桉木,得到81%的未缩合木质素,并保持高达90%的β-O-4键,同时纤维素达到99%的保留率,并保持高聚合度1160,实验数据集未明确提及。