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 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 sub-diffusive 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 MD trajectories from ns to {\mu}s timescales. The obtained self-diffusion coefficient of pure CO2 in CMS membranes derived from 6FDA/BPDA-DAM precursor polymer melt is found in agreement with previous experimental findings. We also extend the TEKMC algorithm to evaluate the mixture diffusivities in binary mixtures to determine the permselectivity of CO2 in CH4 and N2 mixtures. The mixture diffusion coefficient of CO2 ranges from 1.3-7 x 10^{-6} cm6{2}s^{-1} in 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 are significantly lower than that predicted using mixture diffusivity data. Specifically in the case of the CO2:N2 mixture we find that using mixture diffusivities led to permeslectivites 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 simulations, our work shows that simulations with the TEKMC method can be used to reliably evaluate the performance of materials for gas separations.

Subhadeep Dasgupta, Arun K.S, K. Ganapathy Ayappa, Prabal K. Maiti · 2023