合金
材料科学
催化作用
高熵合金
选择性
密度泛函理论
熵(时间箭头)
化学工程
化学物理
热力学
计算化学
冶金
化学
有机化学
物理
工程类
作者
Chinmay Dahale,Sriram Goverapet Srinivasan,Beena Rai
标识
DOI:10.1021/acsami.3c12775
摘要
Multicomponent alloys are promising catalysts for diverse chemical conversions, owing to the ability to tune their vast compositional space to maximize catalytic activity and product selectivity. However, elemental segregation, whereby the surface or grain boundaries of the material are enriched in a few elements, is a physically observed phenomenon in such alloys. Such segregation alters not only the composition but also the kinds of catalytically active sites present at the surface. Thus, elemental segregation, which can be achieved via various processing techniques, can be used as an additional knob in searching for alloy compositions that are both active and selective for a target chemical conversion. We demonstrate this using molecular simulations, machine learning, and Bayesian optimization to search for both random solid solution and “segregated” AgAuCuPdPt alloy compositions that are potentially active and selective for CO reduction reaction (CORR). Finally, we validate our findings by computing the reaction-free energy landscape for the CORR on the optimal alloy compositions via density functional theory calculations.
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