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从木质纤维素生物质中无漂白、耐木质素、高产率生产纳米晶纤维素。

Bleaching-free, lignin-tolerant, high-yield production of nanocrystalline cellulose from lignocellulosic biomass.

作者信息

Li Zipeng, Xie Di, Zhu Weizhi, Wang Hongjie, Ouyang Tulong, Sun Jianping, Wu Yiqiang, Cheng Fangchao

机构信息

Guangxi Key Laboratory of Processing for Nonferrous Metallic and Featured Materials, School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China.

College of Material Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.

出版信息

iScience. 2022 Dec 13;26(1):105771. doi: 10.1016/j.isci.2022.105771. eCollection 2023 Jan 20.

Abstract

Nanocrystalline cellulose (NCC) preparation in an integrated fractionation manner is expected to solve the problems of low yield and environmental impact in the traditional process. An integrated fractionation strategy for NCC production from wood was developed through catalytic biomass fractionation, the partial dissolution of cellulose-rich materials (CRMs) in aqueous tetrabutylphosphonium hydroxide, and short-term ultrasonication. The presented process could tolerate a high CRM lignin content of 21.2 wt % and provide a high NCC yield of 76.6 wt % (34.3 wt % of the original biomass). The increase in the CRM lignin content decreased the NCC yield, facilitated the crystal transition of NCC from cellulose I to cellulose II, and showed no apparent effects on the NCC morphology. A partial/selective dissolution mechanism is proposed for the presented strategy. This study provided a promising efficient fractionation-based method toward comprehensive and high-value utilization of lignocellulosic biomass through effective delignification and high-yield NCC production.

摘要

以集成分级方式制备纳米晶纤维素(NCC)有望解决传统工艺中产量低和环境影响的问题。通过催化生物质分级、富含纤维素材料(CRM)在氢氧化四丁基鏻水溶液中的部分溶解以及短期超声处理,开发了一种从木材生产NCC的集成分级策略。所提出的工艺能够耐受21.2 wt%的高CRM木质素含量,并提供76.6 wt%(占原始生物质的34.3 wt%)的高NCC产量。CRM木质素含量的增加降低了NCC产量,促进了NCC从纤维素I到纤维素II的晶体转变,并且对NCC形态没有明显影响。针对所提出的策略提出了一种部分/选择性溶解机制。本研究通过有效的脱木质素和高产NCC生产,为木质纤维素生物质的综合高值利用提供了一种有前景的基于高效分级的方法。

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