Kinetic Monte Carlo Simulations of Methanol Synthesis from Carbon Dioxide and Hydrogen on Cu(111) Catalysts: Statistical Uncertainty Study

An optimal multiscale linking and integration of atom-scale density functional theory (DFT) computations with mesoscopic kinetic Monte Carlo (KMC) is gaining in importance, particularly upon considering the engineering and intensification of unconventional feedstock processing, as well as the design of emerging catalysis routes. Carbon dioxide activation for methanol synthesis reactions on Cu(111) catalysts was studied using first-principles calculations and KMC modeling simulations. The CO2 hydrogenation pathway model was applied, consisting of the formate and the reverse water–gas shift (RWGS) mechanistic steps. The dependence of conversion, selectivity, and the rate of desorbed bulk CH3OH production upon operating process conditions, primarily temperature and pressure, was examined. Catalytic performance results are qualitatively well comparable with the available literature data for heterogeneous copper-based materials. Furthermore, the numerical stability analysis of KMC simulations was statistically assessed with respect to random seed parameters and activation energy barriers. Surface product distribution was found to be particularly sensitive to the smallest perturbations of the activation standard Gibbs energy. The effects of binding site size, crystal lattice dimensions, packed-bed influx composition (gaseous phase reactant partial pressures), and input randomized numbers were, however, less pronounced. This demonstrates that an accurate evaluation of ab initio theoretical research is crucial, especially upon paralleling them to experimental reactor concentrations.

Authors: Drejc Kopač , Matej Huš, Mitja Ogrizek, and Blaž Likozar (Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia)

DOI: 10.1021/acs.jpcc.7b04985

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement
No 637016.


Cookies are important for the proper functioning of this site. To improve this experience, we use third-party analytic cookies in order to allow us to elaborate statistical information about the user’s activities in this site as well as first party customization cookies to record the user’s acceptance of the use of cookies on this site. By continuing to browse our site, you are agreeing to our use of cookies.OK
For more information on cookies please refer to our privacy and cookie policy.