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合金纳米颗粒的催化活性图

Catalytic Activity Maps for Alloy Nanoparticles.

作者信息

Cao Liang, Mueller Tim

机构信息

Institute of Catalysis, Department of Chemistry, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.

Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.

出版信息

J Am Chem Soc. 2023 Apr 5;145(13):7352-7360. doi: 10.1021/jacs.2c13607. Epub 2023 Mar 27.

Abstract

To enable rational design of alloy nanoparticle catalysts, we develop an approach to generate catalytic activity maps of alloy nanoparticles on a grid of particle size and composition. The catalytic activity maps are created by using a quaternary cluster expansion to explicitly predict adsorbate binding energies on alloy nanoparticles of varying shape, size, and atomic order while accounting for interactions among the adsorbates. This cluster expansion is used in kinetic Monte Carlo simulations to predict activated nanoparticle structures and turnover frequencies on all surface sites. We demonstrate our approach on Pt-Ni octahedral nanoparticle catalysts for the oxygen reduction reaction (ORR), revealing that the specific activity is predicted to be optimized at an edge length of larger than 5.5 nm and a composition of about PtNi and the mass activity is predicted to be optimized at an edge length of 3.3-3.8 nm and a composition of about PtNi.

摘要

为了实现合金纳米颗粒催化剂的合理设计,我们开发了一种方法,以生成粒度和组成网格上合金纳米颗粒的催化活性图。催化活性图是通过使用四元团簇展开来创建的,该展开可明确预测不同形状、大小和原子序的合金纳米颗粒上吸附质的结合能,同时考虑吸附质之间的相互作用。这种团簇展开用于动力学蒙特卡罗模拟,以预测所有表面位点上的活化纳米颗粒结构和周转频率。我们在用于氧还原反应(ORR)的Pt-Ni八面体纳米颗粒催化剂上展示了我们的方法,结果表明,预测比活性在边长大于5.5 nm且组成为约PtNi时达到最佳,而预测质量活性在边长为3.3 - 3.8 nm且组成为约PtNi时达到最佳。

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