Thermal Activation of Methane by MgO$^+$: Temperature Dependent Kinetics, Reactive Molecular Dynamics Simulations and Statistical Modeling
Abstract
The kinetics of MgO + CH was studied experimentally using the variable ion source, temperature adjustable selected ion flow tube (VISTA-SIFT) apparatus from 300 600 K and computationally by running and analyzing reactive atomistic simulations. Rates and product branching fractions were determined as a function of temperature. The reaction proceeded with a rate of K cm s. MgOH was the dominant product at all temperatures, but Mg, the co-product of oxygen-atom transfer to form methanol, was observed with a product branching fraction of K. Reactive molecular dynamics simulations using a reactive force field, as well as a neural network yield rate coefficients about one order of magnitude lower. This underestimation of the rates is traced back to the multireference character of the transition state [MgOCH]. Statistical modeling of the temperature-dependent kinetics provides further insight into the reactive potential surface. The rate limiting step was found to be consistent with a four-centered activation of the C-H bond, consistent with previous calculations. The product branching was modeled as a competition between dissociation of an insertion intermediate directly after the rate-limiting transition state, and traversing a transition state corresponding to a methyl migration leading to a Mg-CHOH complex, though only if this transition state is stabilized significantly relative to the dissociated MgOH + CH product channel. An alternative non-statistical mechanism is discussed, whereby a post-transition state bifurcation in the potential surface could allow the reaction to proceed directly from the four-centered TS to the Mg-CHOH complex thereby allowing a more robust competition between the product channels.
Keywords
Cite
@article{arxiv.2002.02151,
title = {Thermal Activation of Methane by MgO$^+$: Temperature Dependent Kinetics, Reactive Molecular Dynamics Simulations and Statistical Modeling},
author = {Brendan C. Sweeny and Hanqing Pan and Asmaa Kassem and Jordan C Sawyer and Shaun G. Ard and Nicholas S. Shuman and Albert A. Viggiano and Sebastian Brickel and Oliver T. Unke and Meenu Upadhyay and Markus Meuwly},
journal= {arXiv preprint arXiv:2002.02151},
year = {2020}
}