Surface roughening in nanoparticle catalysts

Abstract: Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and characterize their structures and atomic sites that are correlated with high catalytic activity. Identification of these ''active sites'' has relied heavily on extrapolation to supported NP systems from investigation of idealized surfaces, experimentally using single crystals or supported NPs which are always modelled computationally using slab or regular polyhedra models. However, the ability of these methods to predict catalytic activity remains qualitative at best, as the structure of metal NPs in reactive environments has only been speculated from indirect experimental observations, or otherwise remains wholly unknown. Here, by circumventing these limitations for highly accurate simulation methods, we provide direct atomistic insight into the dynamic restructuring of metal NPs by combining in situ spectroscopy with molecular dynamics simulations powered by a machine learned force field. We find that in reactive environments, NP surfaces evolve to a state with poorly defined atomic order, while the core of the NP may remain bulk-like. These insights prove that long-standing conceptual models based on idealized faceting for small metal NP systems are not representative of real systems under exposure to reactive environments. We show that the resultant structure can be elucidated by combining advanced spectroscopy and computational tools. This discovery exemplifies that to enable faithful quantitative predictions of catalyst function and stability, we must move beyond idealized-facet experimental and theoretical models and instead employ systems which include realistic surface structures that respond to relevant physical and chemical conditions.