Trajectory-Extending Kinetic Monte Carlo Simulations to Evaluate Pure and Gas Mixture Diffusivities through a Dense Polymeric Membrane

With renewed interest in CO2 separations, carbon molecular sieving (CMS) membrane performance evaluation requires diffusion coefficients as inputs to have a reliable estimate of the permeability. An optimal material is desired to have both high selectivity and permeability. Gases diffusing through dense CMS and polymeric membranes experience extended subdiffusive regimes, which hinders reliable extraction of diffusion coefficients from mean squared displacement data. We improve the sampling of the diffusive landscape by implementing the trajectory-extending kinetic Monte Carlo (TEKMC) technique to efficiently extend molecular dynamics (MD) trajectories from ns to mu s time scales. The obtained self-diffusion coefficient of pure CO2 in CMS membranes derived from a 6FDA/BPDA-DAM precursor polymer melt is found to linearly increase from 0.8-1.3 x 10(-6) cm(2) s(-1) in the pressure range of 1-20 bar, which supports previous experimental findings. We also extended the TEKMC algorithm to evaluate the mixture diffusivities in binary mixtures to determine the permselectivity of CO(2 )in CH4 and N-2 mixtures. The mixture diffusion coefficient of CO2 ranges from 1.3-7 x 10(-6) cm(2) s(-1) in the binary mixture CO2/CH4, which is significantly higher than the pure gas diffusion coefficient. Robeson plot comparisons show that the permselectivity obtained from pure gas diffusion data is significantly lower than that predicted using mixture diffusivity data. Specifically, in the case of the CO2/N(2 )mixture, we find that using mixture diffusivities led to permselectivities lying above the Robeson limit highlighting the importance of using mixture diffusivity data for an accurate evaluation of the membrane performance. Combined with gas solubilities obtained from grand canonical Monte Carlo (GCMC) simulations, our work shows that simulations with the TEKMC method can be used to reliably evaluate the performance of materials for gas separations.

Automated summary

With the renewed interest in CO2 separations, this study focuses on evaluating the performance of carbon molecular sieving (CMS) membranes by determining diffusion coefficients. By implementing the trajectory-extending kinetic Monte Carlo (TEKMC) technique, molecular dynamics trajectories were extended to accurately estimate diffusion coefficients and permeability of gases in CMS and polymeric membranes. The study revealed the linear increase of pure CO2 self-diffusion coefficient in CMS membranes under different pressures and also evaluated mixture diffusivities of CO2 in binary mixtures. The importance of using mixture diffusivity data for accurate evaluation of membrane performance was highlighted.