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With renewed interest in CO2 separations, carbon molecular sieving (CMS)membrane performance evaluation requires diffusion coefficients as inputs tohave reliable estimate of the permeability. An optimal material is desired tohave both high selectivity and permeability. Gases diffusing through dense, CMSand polymeric membranes experience extended sub-diffusive regimes which hindersreliable extraction of diffusion coefficients from mean squared displacementdata. We improve the sampling of the diffusive landscape by implementing thetrajectory extending kinetic Monte Carlo (TEKMC) technique to efficientlyextend MD trajectories from ns to {\mu}s timescales. The obtainedself-diffusion coefficient of pure CO2 in CMS membranes derived from6FDA/BPDA-DAM precursor polymer melt is found in agreement with previousexperimental findings. We also extend the TEKMC algorithm to evaluate themixture diffusivities in binary mixtures to determine the permselectivity ofCO2 in CH4 and N2 mixtures. The mixture diffusion coefficient of CO2 rangesfrom 1.3-7 x 10^{-6} cm6{2}s^{-1} in binary mixture CO2:CH4 which issignificantly higher than the pure gas diffusion coefficient. Robeson plotcomparisons show that the permselectivity obtained from pure gas diffusion dataare significantly lower than that predicted using mixture diffusivity data.Specifically in the case of the CO2:N2 mixture we find that using mixturediffusivities led to permeslectivites lying above the Robeson limithighlighting the importance of using mixture diffusivity data for an accurateevaluation of the membrane performance. Combined with gas solubilities obtainedfrom grand-canonical Monte Carlo simulations, our work shows that simulationswith the TEKMC method can be used to reliably evaluate the performance ofmaterials for gas separations.

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