4.7 Article

One-step peracetic acid pretreatment of hardwood and softwood biomass for platform chemicals production

期刊

SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

出版社

NATURE RESEARCH
DOI: 10.1038/s41598-021-90667-9

关键词

-

资金

  1. Australian Government Research Training Program (RTP) Scholarship

向作者/读者索取更多资源

The study demonstrates that thermally-assisted peracetic acid pretreatment of lignocellulosic biomass effectively removes lignin with negligible loss of carbohydrates, achieving a 90% delignification rate. It significantly improves biomass digestibility, increasing glucan content, especially when conducted under mild temperature conditions.
Lignocellulosic biomass is an attractive renewable resource to produce biofuel or platform chemicals. Efficient and cost-effective conversion systems of lignocellulosic biomass depend on their appropriate pretreatment processes. Alkali or dilute acid pretreatment of biomass requires a high temperature (>150 degrees C) to remove xylan (hemicellulosic sugar) and lignin partially. In this study, peracetic acid was used to pretreat biomass feedstocks, including hardwood and softwood species. It was found that the thermally-assisted dilute acid pretreatment of biomass conducted under the mild temperature of 90 degrees C up to 5 h resulted in the effective removal of lignin from the biomass with a negligible loss of carbohydrates. This thermally-assisted pretreatment achieved 90% of delignification, and this result was compared with the microwave-assisted pretreatment method. In addition, the crystallinity index (CrI), surface morphology, and chemical structure were significantly changed after the acid pretreatment. The biomass digestibility increased significantly with increased reaction time, by 32% and 23% for hardwood and softwood, respectively. From this study, it is clear that peracetic acid pretreatment is an effective method to enrich glucan content in biomass by delignification.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据