4.8 Article

Selective and effective oxidation of 5-hydroxymethylfurfural by tuning the intermediates adsorption on Co-Cu-CNx

期刊

NANO RESEARCH
卷 16, 期 5, 页码 6670-6678

出版社

TSINGHUA UNIV PRESS
DOI: 10.1007/s12274-023-5450-3

关键词

Co -based materials; 5-hydroxymethylfurfural (HMF) oxidative activity; 2,5-furandicarboxylic acid (FDCA) selectivity; electronegativity offset; d -band center

向作者/读者索取更多资源

N-doped carbon-supported Co-based dual-metal nanoparticles exhibit enhanced catalytic activity and selectivity for the oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA). Among them, Co-Cu-1.4-CNx shows 4 times higher FDCA formation rate than pristine Co-CN, with 100% FDCA selectivity. Density functional theory (DFT) calculations reveal that the increased electron density on Co sites induced by Cu reduces the energy barriers for HMF conversion to FDCA. These findings contribute to the development of superior non-precious metal catalysts for HMF oxidation.
Co -based catalysts are promising alternatives to precious metals for the selective and effective oxidation of 5hydroxymethylfurfural (HMF) to the higher value-added 2,5-furandicarboxylic acid (FDCA). However, these catalysts still suffer from unsatisfactory activity and poor selectivity. A series of N -doped carbon -supported Co -based dual -metal nanoparticles (NPs) have been designed, among which the Co-Cu-1.4-CNx, exhibits enhanced HMF oxidative activity, achieving FDCA formation rates 4 times higher than that of pristine Co-CN, with 100% FDCA selectivity. Density functional theory (DFT) calculations evidenced that the increased electron density on Co sites induced by Cu can mediate the positive electronegativity offset to downshift the d band center of Co-Cu-1.4-CNx, thus reducing the energy barriers for the conversion of HMF to FDCA. Such findings will support the development of superior non-precious metal catalysts for HMF oxidation.

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

4.8
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