Suppr超能文献

用于高效选择性金属氮掺杂碳催化剂电催化还原CO的有效筛选途径

Effective Screening Route for Highly Active and Selective Metal-Nitrogen-Doped Carbon Catalysts in CO Electrochemical Reduction.

作者信息

Park Byoung Joon, Wang Ying, Lee Yechan, Noh Kyung-Jong, Cho Ara, Jang Myeong Gon, Huang Rui, Lee Kug-Seung, Han Jeong Woo

机构信息

Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.

Beamline Division, Pohang Accelerator Laboratory (PAL), Pohang, Gyeongbuk, 37673, Republic of Korea.

出版信息

Small. 2021 Oct;17(42):e2103705. doi: 10.1002/smll.202103705. Epub 2021 Sep 23.

Abstract

To identify high-efficiency metal-nitrogen-doped (M-N-C) electrocatalysts for the electrochemical CO -to-CO reduction reaction (CO RR), a method that uses density functional theory calculation is presented to evaluate their selectivity, activity, and structural stability. Twenty-three M-N -C catalysts are evaluated, and three of them (M = Fe, Co, or Ni) are identified as promising candidates. They are synthesized and tested as proof-of-concept catalysts for CO -to-CO conversion. Different key descriptors, including the maximum reaction energy, differences of the *H and *CO binding energy (ΔG -ΔG ), and *CO desorption energy (ΔG ), are used to clarify the reaction mechanism. These computational descriptors effectively predict the experimental observations in the entire range of electrochemical potential. The findings provide a guideline for rational design of heterogeneous CO RR electrocatalysts.

摘要

为了识别用于电化学CO还原反应(CO RR)的高效金属氮掺杂(M-N-C)电催化剂,提出了一种使用密度泛函理论计算的方法来评估其选择性、活性和结构稳定性。对23种M-N-C催化剂进行了评估,其中三种(M = Fe、Co或Ni)被确定为有前景的候选物。它们被合成并作为CO转化为CO的概念验证催化剂进行测试。使用不同的关键描述符,包括最大反应能量、H和CO结合能的差异(ΔG -ΔG )以及*CO脱附能(ΔG ),来阐明反应机理。这些计算描述符有效地预测了整个电化学电位范围内的实验观察结果。这些发现为合理设计非均相CO RR电催化剂提供了指导。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验