合成气
选择性
尖晶石
锌
催化作用
三元运算
无机化学
化学
金属
氧化物
合金
化学工程
材料科学
铬
冶金
有机化学
程序设计语言
工程类
计算机科学
作者
Sicong Ma,Sida Huang,Zhi‐Pan Liu
出处
期刊:Nature Catalysis
[Springer Nature]
日期:2019-06-17
卷期号:2 (8): 671-677
被引量:119
标识
DOI:10.1038/s41929-019-0293-8
摘要
Metal oxide alloys (for example AxByOz) exhibit dramatically different catalytic properties in response to small changes in composition (the A:B ratio). Here, we show that for the ternary zinc–chromium oxide (ZnCrO) catalysts the activity and selectivity during syngas (CO/H2) conversion strongly depend on the Zn:Cr ratio. By using a global neural network potential, stochastic surface walking global optimization and first principles validation, we constructed a thermodynamics phase diagram for Zn–Cr–O that reveals the presence of a small stable composition island, that is, Zn:Cr:O = 6:6:16 to 3:8:16, where the oxide alloy crystallizes into a spinel phase. By changing the Zn:Cr ratio from 1:2 to 1:1, the ability to form oxygen vacancies increases appreciably and extends from the surface to the subsurface, in agreement with previous experiments. This leads to the critical presence of a four-coordinated planar Cr2+ cation that markedly affects the syngas conversion activity and selectivity to methanol, as further proved by microkinetics simulations. Metal oxide alloys are important industrial catalysts, but their structure–activity relationships are poorly understood. Now, a study encompassing a combination of computational tools and machine learning approaches sheds light on the activity and selectivity of zinc–chromium oxides during syngas conversion.
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