Modeling Morphology and Catalytic Activity of Nanoparticle Ensembles Under Reaction Conditions

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.