Niu Xiangfu, Zhen Shiyu, Zhang Rui, Li Jianqiu, Zhang Liang
Center for Combustion Energy, Tsinghua University Beijing 100084 China
School of Vehicle and Mobility, Tsinghua University Beijing 100084 China.
Chem Sci. 2025 Jul 17. doi: 10.1039/d5sc04043d.
PtCo intermetallic alloy nanoparticles are highly active and stable catalysts for the oxygen reduction reaction (ORR), making them key materials for proton-exchange membrane fuel cells. However, the high-temperature annealing required for ordering into the intermetallic phase often leads to particle growth. In this work, we developed a machine learning interatomic potential to model the disorder-to-order transition in PtCo-based ternary alloys with high accuracy and computational efficiency. Monte Carlo simulations reveal that introducing a third element significantly affects both the ordering process and the critical temperature for the disorder-to-order transition. The thermodynamic driving forces for ordering in various PtCoM alloys were systematically investigated to identify potential high-performance PtCoM catalysts. Kinetic analysis further indicates that the accelerated ordering transition in PtCo alloys is primarily driven by lower migration energy barriers and enhanced directional diffusion. These findings provide valuable atomic-scale insights into the chemical ordering mechanisms and suggest a pathway for designing highly ordered PtCo-based nanoparticles for energy conversion and storage applications.
铂钴金属间化合物合金纳米颗粒是氧还原反应（ORR）的高活性和稳定催化剂，使其成为质子交换膜燃料电池的关键材料。然而，有序排列成金属间相所需的高温退火通常会导致颗粒生长。在这项工作中，我们开发了一种机器学习原子间势，以高精度和计算效率对铂钴基三元合金中的无序到有序转变进行建模。蒙特卡罗模拟表明，引入第三种元素会显著影响有序过程和无序到有序转变的临界温度。系统地研究了各种铂钴M合金中有序排列的热力学驱动力，以确定潜在的高性能铂钴M催化剂。动力学分析进一步表明，铂钴合金中加速的有序转变主要由较低的迁移能垒和增强的定向扩散驱动。这些发现为化学有序机制提供了有价值的原子尺度见解，并为设计用于能量转换和存储应用的高度有序铂钴基纳米颗粒提供了一条途径。
