Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles under Reaction Conditions

Abstract

Although nanoparticle catalysts obtain different sizes and shapes under reaction conditions, computational modeling in heterogeneous catalysis is usually based on well-defined crystallographic planes. Herein, we combine density functional theory (DFT) calculations with Boltzmann statistics to describe ensembles of nanoparticles obtaining different morphologies under reaction conditions (temperature and gas-phase chemical potential) and their respective distribution of active sites. We apply our methodology on Rh catalytic nanoparticles, and we address the contribution of metastable nanostructures on the overall CO dissociation catalytic activity. Importantly, we demonstrate how catalytic trends can change when accounting for an ensemble of nanoparticles compared to a single, thermodynamically stable nanoparticle. Thus, our work enlightens the impact of statistical representation of catalyst morphology on modeling structure-sensitive reactions.