Elementary steps of the catalytic NOx reduction with NH3: Cluster studies on adsorbate diffusion and dehydrogenation at vanadium oxide substrate

The Conundrum of "Pair Sites" in Langmuir-Hinshelwood Reaction Kinetics in Heterogeneous Catalysis

Understanding reaction kinetics is crucial for designing and applying heterogeneous catalytic processes in chemical and energy conversion. Here, we revisit the Langmuir-Hinshelwood (L-H) kinetic model for bimolecular surface reactions, originally formulated for metal catalysts, assuming immobile adsorbates on neighboring pair sites, with the rate varying linearly with the density of surface sites (sites per unit area); r ∝ [*]o 1. Supported metal oxide catalysts, however, offer systematic control over [*]o through variation of the active two-dimensional metal oxide loading in the submonolayer region. Various reactions catalyzed by supported metal oxides are analyzed, such as supported VO x catalysts, including methanol oxidation, oxidative dehydrogenation of propane and ethane, SO2 oxidation to SO3, propene oxidation to acrolein, n-butane oxidation to maleic anhydride, and selective catalytic reduction of nitric oxide with ammonia. The analysis reveals diverse dependencies of reaction rate on [*]o for these surface reactions, with r ∝ [*]o n , where n equals 1 for reactions with a unimolecular rate-determining step and 2 for those with a bimolecular rate-limiting step or exchange of more than 2 electrons. We propose refraining from a priori assumptions about the nature and density of surface sites or adsorbate behavior, advocating instead for data-driven elucidation of kinetics based on the density of surface sites, adsorbate coverage, etc. Additionally, recent studies on catalytic surface mechanisms have shed light on nonadjacent catalytic sites catalyzing surface reactions in contrast to the traditional requirement of adjacent/pair sites. These findings underscore the need for a more nuanced approach in modeling heterogeneous catalysis, especially supported metal oxide catalysts, encouraging reliance on experimental data over idealized assumptions that are often difficult to justify.