Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning†. Issue 11 (6th September 2021)

• Record Type: Journal Article
• Authors: Chen, Dingming; Lai, Zhuangzhuang; Zhang, Jiawei; Chen, Jianfu; Hu, Peijun; Wang, Haifeng
• Is Part Of: Chinese journal of chemistry. Volume 39:Issue 11(2021)
• Page Start: 3029
• Page End: 3036
• Publication Date: 2021-09-06
• Subjects: Machine learning -- Density functional calculations -- Genetic algorithm -- Nanostructures -- O—O activation; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/cjoc.202100352
• Languages: English
• ISSNs: 1001-604X

Abstract

Main observation and conclusion: 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 Au13 Pt42 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. Abstract : To elucidate the reconstruction mechanism of the bimetallic Au@Pt nanocluster and its catalytic implications, we chose the 55‐atom Au13 Pt42 core‐shell icosahedron as the initial state and combined the machine learning potential, genetic algorithm, and DFT validation to predict the stable configuration of reconstruction. It turns out that deformation and segregation coexist in the reconstruction process, and it is the synergetic effect of deformation and segregation that gives rise to the enhancement of ORR activity.