Predicting Adhesion Energies of Metal Nanoparticles to Support Surfaces, Which Determines Metal Chemical Potential versus Particle Size and Thus Catalyst Performance

作者：Kun Zhao, Daniel J. Auerbach, Charles T. Campbell · 发表于：ACS Catalysis · 年份：2024 · DOI：10.1021/acscatal.4c02559 · 被引用次数：16 · 研究领域：Catalytic Processes in Materials Science、Electrocatalysts for Energy Conversion、Machine Learning in Materials Science

Improved catalysts and electrocatalysts composed of transition metal nanoparticles dispersed on high-area supports are essential for energy and environmental technologies. The chemical potential of the metal atoms in these supported nanoparticles is an important descriptor that correlates with both their catalytic activity and deactivation rate. This descriptor (μ M ) is predictably determined by the particle size and the adhesion energy per unit area at the metal/support interface ( E adh ). We show here that the adhesion energies for different metals on a given support scale linearly with a simple property of the metal: for oxides, it is proportional to the metal oxophilicity, and for the carbon support, it increases linearly with metal carbophilicity (both divided by the area per metal atom). These relationships allow predicting E adh for other metal/support combinations, thus allowing estimation of μ M versus particle size and thereby better structure-based predictions of catalysts’ performance, which can aid in designing improved catalysts.