Abstract
Modeling the kinetics of surface catalyzed reactions is essential for the design of reactors and chemical processes. The majority of microkinetic models employ mean-field approximations, which lead to an approximate description of catalytic kinetics by assuming spatially uncorrelated adsorbates. On the other hand, kinetic Monte Carlo (KMC) methods provide a discrete-space continuous-time stochastic formulation that enables an accurate treatment of spatial correlations in the adlayer, but at a significant computation cost. In this work, we use the so-called cluster mean-field approach to develop higher order approximations that systematically increase the accuracy of kinetic models by treating spatial correlations at a progressively higher level of detail. We further demonstrate our approach on a reduced model for NO oxidation incorporating first nearest-neighbor lateral interactions and construct a sequence of approximations of increasingly higher accuracy, which we compare with KMC and mean-field. The latter is found to perform rather poorly, overestimating the turnover frequency by several orders of magnitude for this system. On the other hand, our approximations, while more computationally intense than the traditional mean-field treatment, still achieve tremendous computational savings compared to KMC simulations, thereby opening the way for employing them in multiscale modeling frameworks.
Highlights
Catalyzed reactions are widely used in the chemical industry, and in everyday applications
Examples of such applications range from petroleum refining to automotive emission control.1. In this type of catalysis, the reactants adsorb onto the catalyst surface, via the formation of chemical or physical bonds
We focus on the development of a hierarchy of approximations, based on the so-called cluster mean-field approximation for kinetic lattice-gas (LG) models,20,21 that allow us to calculate catalytic rates or turnover frequencies (TOFs) in an accurate and efficient way
Summary
Catalyzed reactions are widely used in the chemical industry, and in everyday applications. A central aspect of this endeavor is to understand the elementary reaction steps and model the dynamics of catalytic processes To this end, kinetic modeling approaches, such as microkinetic mean-field (MKM) models or the so-called kinetic Monte Carlo (KMC) simulation, are of paramount importance. Traditional MKM models are highly efficient, they may lead to inaccurate predictions because they ignore details about the spatial correlations in the adlayer Such correlations can arise from slow diffusion in tandem with reaction or from adsorbate-adsorbate lateral interactions.. There have been some attempts to improve the accuracy of microkinetic models to better approximate the master equation.17,18 These approaches generalize traditional MKM methods by introducing an infinite set of evolution equations for spatial correlations, which is truncated at some level using moment closure techniques.
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