Unveiling “Sabatier principle” for electrocatalytic nitric oxide reduction on single cluster catalysts: A DFT and machine learning guideline

催化作用 化学 选择性催化还原 星团(航天器) 组合化学 计算化学 纳米技术 计算机科学 材料科学 有机化学 程序设计语言
作者
Lei Yang,Jiake Fan,Beibei Xiao,Weihua Zhu
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:468: 143823-143823 被引量:36
标识
DOI:10.1016/j.cej.2023.143823
摘要

As a revolutionary chemical engineering, the electrocatalytic synthesis of ammonia (NH3) has become one alternative option for green hydrogen storage. However, under the guidance of Sabatier principle, favored single atom catalyst (SAC) hardly gets rid of the perplexity of element selection. Here, a span-new structure-activity correlation for nitric oxide reduction reaction (NORR) was well-proposed on single cluster catalyst (SCC), which not only contributes to improve the activity with the cluster growth, but also breaks the consistent pattern of catalytic performance toward designed SAC. By performing DFT calculations, Co4@GaS was selected out because of its unexceptionable activity, only −0.06 V for working potential rarely reported yet. More eye-catching, as the displacement of activity descriptors obtained from elusory electronic characteristics only, a universal expression that handles free energy change was put forward with the assistance of machine learning (ML). This work may open a fresh avenue for the rational and straightforward design of desirable catalysts and even the development of interdisciplinary in chemistry.
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