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

Dasgupta, S and Arun, KS and Ayappa, KG and Maiti, PK (2023) Trajectory-Extending Kinetic Monte Carlo Simulations to Evaluate Pure and Gas Mixture Diffusivities through a Dense Polymeric Membrane. In: Journal of Physical Chemistry B, 127 (45). pp. 9841-9849.

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Abstract

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 μs 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 � 10-6 cm2 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 CO2 in CH4 and N2 mixtures. The mixture diffusion coefficient of CO2 ranges from 1.3-7 � 10-6 cm2 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/N2 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. © 2023 American Chemical Society.

Item Type:	Journal Article
Publication:	Journal of Physical Chemistry B
Publisher:	American Chemical Society
Additional Information:	The copyright for this article belongs to authors.
Keywords:	Binary mixtures; Carbon dioxide; Diffusion in gases; Diffusion in liquids; Gas permeable membranes; Gases; Intelligent systems; Monte Carlo methods; Polymer melts; Trajectories, CH 4; Gas diffusion; Gases mixture; Kinetic Monte Carlo; Kinetic monte carlo simulation; Membrane performance; Molecular sieving; Performances evaluation; Permselectivities; Pure gas, Molecular dynamics
Department/Centre:	Division of Mechanical Sciences > Chemical Engineering Division of Physical & Mathematical Sciences > Physics
Date Deposited:	29 Nov 2024 11:18
Last Modified:	29 Nov 2024 11:18
URI:	http://eprints.iisc.ac.in/id/eprint/85368

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