Predicting Adhesion\nEnergies of Metal Nanoparticles\nto Support Surfaces, Which Determines Metal Chemical Potential versus\nParticle Size and Thus Catalyst Performance

Improved catalysts and electrocatalysts composed of transition\nmetal nanoparticles dispersed on high-area supports are essential\nfor energy and environmental technologies. The chemical potential\nof the metal atoms in these supported nanoparticles is an important\ndescriptor that correlates with both their catalytic activity and\ndeactivation rate. This descriptor (μ<sub>M</sub>) is predictably\ndetermined by the particle size and the adhesion energy per unit area\nat the metal/support interface (<i>E</i><sub>adh</sub>).\nWe show here that the adhesion energies for different metals on a\ngiven support scale linearly with a simple property of the metal:\nfor oxides, it is proportional to the metal oxophilicity, and for\nthe carbon support, it increases linearly with metal carbophilicity\n(both divided by the area per metal atom). These relationships allow\npredicting <i>E</i><sub>adh</sub> for other metal/support\ncombinations, thus allowing estimation of μ<sub>M</sub> versus\nparticle size and thereby better structure-based predictions of catalysts’\nperformance, which can aid in designing improved catalysts.