Catalytic and Inhibitory Effects Induced by Noncovalent Interactions between Cellulose and Lignin during Fast Pyrolysis

木质素 纤维素 左旋葡糖 生物量(生态学) 化学 热解 半纤维素 有机化学 木质纤维素生物量 化学工程 催化作用 海洋学 地质学 工程类 气溶胶 生物质燃烧
作者
Fuat Sakirler,Mesut Tekbaş,Hsi‐Wu Wong
出处
期刊:ACS Sustainable Chemistry & Engineering [American Chemical Society]
卷期号:12 (26): 9591-9601
标识
DOI:10.1021/acssuschemeng.4c00481
摘要

Biomass fast pyrolysis has emerged as a highly promising technology for producing renewable fuels and chemicals. However, the inherent multiscale and multiphase nature of the process and the heterogeneous nature of biomass feedstocks typically lead to low selectivity toward each bio-oil molecule, posing significant commercialization challenges. A molecular-level understanding of the biomass pyrolysis reaction kinetics considering the interactions between the main constituents (i.e., cellulose, hemicellulose, and lignin) is essential to advance the macroscopic design, scale-up, and optimization of the process. In this work, microreactor experiments were conducted to determine the effects of lignin structures on the yields of cellulose-derived products during pyrolysis. We show that levoglucosan formation is inhibited by the β-O-4 lignin linkages or catalyzed by the 5-5 linkages, glycolaldehyde formation is catalyzed by the β-O-4 linkages or inhibited by the 5-5 linkages, and 5-hydroxymethylfurfural formation is inhibited by either linkage. Density functional theory calculations reveal that these catalytic and inhibitory effects on cellulose fast pyrolysis are induced by noncovalent interactions between cellulose and lignin. The molecular-level picture of cellulose–lignin interactions uncovered in this work paves the way for further use of genetic engineering to grow new genotypes of biomass for selective production of value-added chemicals and machine learning approaches to obtain correlations between biomass structures and product yields for biomass fast pyrolysis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
Aletta发布了新的文献求助50
1秒前
林沐发布了新的文献求助10
2秒前
苯酚装醇完成签到,获得积分10
3秒前
南瓜饼发布了新的文献求助10
3秒前
爆米花应助含蓄海白采纳,获得10
3秒前
O基米德发布了新的文献求助10
4秒前
5秒前
6秒前
9秒前
9秒前
缥缈白翠完成签到,获得积分10
9秒前
苯酚装醇发布了新的文献求助10
9秒前
oil发布了新的文献求助10
10秒前
10秒前
爆米花应助喜悦非笑采纳,获得10
12秒前
想要毕业发布了新的文献求助10
13秒前
14秒前
bhd发布了新的文献求助10
14秒前
peng发布了新的文献求助10
14秒前
14秒前
ddsgsd发布了新的文献求助10
15秒前
tommmmmm15完成签到,获得积分10
15秒前
bjl发布了新的文献求助10
16秒前
爆米花应助Aletta采纳,获得10
16秒前
17秒前
17秒前
18秒前
任天野应助6217采纳,获得10
18秒前
含蓄海白发布了新的文献求助10
19秒前
20秒前
21秒前
科研通AI2S应助oil采纳,获得10
22秒前
shorewong发布了新的文献求助10
22秒前
停停走走发布了新的文献求助10
22秒前
Bystander完成签到 ,获得积分10
23秒前
悦耳蜡烛完成签到,获得积分10
24秒前
jlh发布了新的文献求助10
24秒前
yiqingLin完成签到,获得积分10
24秒前
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6018277
求助须知:如何正确求助?哪些是违规求助? 7606036
关于积分的说明 16158788
捐赠科研通 5165862
什么是DOI,文献DOI怎么找? 2765091
邀请新用户注册赠送积分活动 1746618
关于科研通互助平台的介绍 1635321