Coverage- and Facet-Dependent Multiscale Modeling of O* and H* Adsorption on Pt Catalytic Nanoparticles

Within computational heterogeneous catalysis, two critical factors exist─coverage and multifaceted effects─which are challenging to incorporate and contribute to differences between the results obtained from computational and experimental studies. Such disparities exist when significant adsorbate–adsorbate interactions are present, particularly when coupled with computationally limited facet sampling. Here, we designed a study to demonstrate the significance of coverage and facet effects on the predicted coverages for O* and H* on Pt nanoparticles. This is accomplished by employing multiscale modeling techniques using a three-pronged approach consisting of density functional theory (DFT), ab initio phase diagrams, and mean-field microkinetic models. Overall, adsorbate–adsorbate interactions are repulsive and far stronger for O*/Pt than for H*/Pt. For O* on Pt(111), repulsive interactions are both two- and three-body, but on Pt(100) and Pt(110), they are predominantly three-body. Through benchmarks to existing experimental literature, we demonstrate that experimentally observed coverages and desorption temperatures can be accurately estimated by computational models when the adsorbate–adsorbate interactions are included. Finally, by combining microkinetic models at the equilibrium limit with kubic harmonic interpolation, we model the impacts of the treatment of adsorbate–adsorbate interactions on the predicted O* and H* coverages over multifaceted Pt nanoparticles. Omitting adsorbate–adsorbate interactions over Pt nanoparticles leads to an overestimation of equilibrium coverages of the adsorbates (maximum of 0.29 and 0.11 ML for O* and H*, respectively) across a wide range of temperatures and pressures relevant to heterogeneous catalysis. Altogether, our work demonstrates that the inclusion of coverage and facet effects increases the accuracy of computational models for heterogeneous catalysts.