Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster:Insights from Machine Learning  

作　　者：Dingming Chen Zhuangzhuang Lai Jiawei Zhang Jianfu Chen Peijun Hu Haifeng Wang 

机构地区：[1]Key Laboratory for Advanced Materials,Centre for Computational Chemistry and Research Institute of Industrial Catalysis,School of Chemistry and Molecular Engineering,East China University of Science and Technology,Shanghai,200237 China

出　　处：《Chinese Journal of Chemistry》2021年第11期3029-3036,共8页中国化学（英文版）

基　　金：This project was supported by the National Natural Science Foundation of China(Nos.21873028,91945302);the National Ten Thousand Talent Program for Young Top-notch Talents in China,Shanghai ShuGuang Project(No.17SG30);the Fundamental Research Funds for the Central Universities.

摘　　要：As a common electrocatalytic system,Au-Pt alloy particles are often prepared as Au-core-Pt-shell(Au@Pt)to make full use of platinum.However,Au has a strong tendency to segregate to the outer surface,leading to the redistribution of the active sites.Unfortunately,the mechanism of such reconstruction and its effect on the electrocatalytic activity have not been thoroughly discussed,largely owing to the complexity of in-situ characterization and computational modeling.Herein,by taking the 55-atom Au13Pt42 core-shell nanocluster as an example,we utilized the neural network potential at density functional theory(DFT)level and the genetic algorithm to search the complex global configurational space.It turns out that it is thermodynamically favorable when all gold atoms are segregated to the surface and the shape of the cluster tends to change from icosahedron to a distorted amorphous structure(at a reduced core,DRC)with a unique gold distribution.Towards understanding the dynamic activity variation of oxygen reduction reaction(ORR)on this bimetallic Au@Pt system,oxygen adsorption energy calculations show that this reconstruction could not only increase the number of adsorption sites but also dramatically improve the ORR catalytic activity of each site,thus enhance the overall ORR reactivity.

关 键 词：Machine learning Density functional calculations Genetic algorithm NANOSTRUCTURES O-O activation 

分 类 号：O64[理学—物理化学]