标题： 轨迹扩展动力蒙特卡罗模拟以评估纯物质和气体混合物通过致密聚合物膜的扩散系数 显示英文标题

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

摘要： 随着对CO2分离的重新关注，碳分子筛（CMS）膜性能评估需要扩散系数作为输入，以获得渗透性的可靠估计。理想的材料应同时具有高选择性和渗透性。通过致密CMS和聚合物膜扩散的气体经历延长的亚扩散区域，这阻碍了从均方位移数据中可靠地提取扩散系数。我们通过实施轨迹扩展动力蒙特卡罗（TEKMC）技术，有效地将MD轨迹从纳秒扩展到{\mu }秒的时间尺度，从而改进了扩散景观的采样。从6FDA/BPDA-DAM前体聚合物熔体得到的纯CO2在CMS膜中的自扩散系数与之前实验结果一致。我们还将TEKMC算法扩展到评估二元混合物中的混合扩散系数，以确定CO2在CH4和N2混合物中的选择性。CO2在二元混合物CO2:CH4中的混合扩散系数范围为1.3-7 x 10^{-6}cm6{2}s^{-1}，这明显高于纯气体扩散系数。Robeson图比较显示，从纯气体扩散数据获得的选择性显著低于使用混合扩散系数数据预测的结果。特别是对于CO2:N2混合物，我们发现使用混合扩散系数导致的渗透选择性高于Robeson极限，突显了使用混合扩散系数数据对准确评估膜性能的重要性。结合从巨正则蒙特卡罗模拟中获得的气体溶解度，我们的研究表明，使用TEKMC方法的模拟可以用于可靠地评估材料在气体分离中的性能。 显示英文摘要

摘要： 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.