Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane

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Abstract

Kinetic\nparameters for surface reactions can be predicted using\na combination of density functional theory calculations, scaling relations,\nand machine learning algorithms; however, construction of microkinetic\nmodels still requires a knowledge of all the possible, or at least\nreasonable, reaction pathways. The recently developed reaction mechanism\ngenerator (RMG) for heterogeneous catalysis, now included in RMG version\n3.0, is built upon well-established, open-source software that can\nprovide detailed reaction mechanisms from user-supplied initial conditions\nwithout making <i>a priori</i> assumptions. RMG is now able\nto estimate adsorbate thermochemistry and construct detailed microkinetic\nmodels on a range of hypothetical metal surfaces using linear scaling\nrelationships. These relationships are a simple, computationally efficient\nway to estimate adsorption energies by scaling the energy of a calculated\nsurface species on one metal to any other metal. By conducting simulations\nwith sensitivity analyses, users can not only determine the rate-limiting\nstep on each surface by plotting a “volcano surface”\nfor the degree of rate control of each reaction as a function of elemental\nbinding energies but also screen novel catalysts for desirable properties.\nWe investigated the catalytic partial oxidation of methane to demonstrate\nthe utility of this new tool and determined that an inlet gas C/O\nratio of 0.8 on a catalyst with carbon and oxygen binding energies\nof −6.75 and −5.0 eV, respectively, yields the highest\namount of synthesis gas. Sensitivity analyses show that while the\ndissociative adsorption of O<sub>2</sub> has the highest degree of\nrate control, the interactions between individual reactions and reactor\nconditions are complex, which result in a dynamic rate-limiting step\nacross differing metals.

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