Abstract

Several thousands of metal organic frameworks (MOFs) have been reported to date, but the information on H2/N2 separation performances of MOF membranes is currently very limited in the literature. We report the first large-scale computational screening study that combines state-of-the-art molecular simulations, grand canonical Monte Carlo (GCMC) and molecular dynamics (MD), to predict H2 permeability and H2/N2 selectivity of 3765 different types of MOF membranes. Results showed that MOF membranes offer very high H2 permeabilities, 2.5 × 103 to 1.7 × 106 Barrer, and moderate H2/N2 membrane selectivities up to 7. The top 20 MOF membranes that exceed the polymeric membranes’ upper bound for H2/N2 separation were identified based on the results of initial screening performed at infinite dilution condition. Molecular simulations were then carried out considering binary H2/N2 and quaternary H2/N2/CO2/CO mixtures to evaluate the separation performance of MOF membranes under industrial operating conditions. Lower H2 permeabilities and higher N2 permeabilities were obtained at binary mixture conditions compared to the ones obtained at infinite dilution due to the absence of multicomponent mixture effects in the latter. Structure–performance relations of MOFs were also explored to provide molecular-level insights into the development of new MOF membranes that can offer both high H2 permeability and high H2/N2 selectivity. Results showed that the most promising MOF membranes generally have large pore sizes (>6 Å) as well as high surface areas (>3500 m2/g) and high pore volumes (>1 cm3/g). We finally examined H2/N2 separation potentials of the mixed matrix membranes (MMMs) in which the best MOF materials identified from our high-throughput screening were used as fillers in various polymers. Results showed that incorporation of MOFs into polymers almost doubles H2 permeabilities and slightly enhances H2/N2 selectivities of polymer membranes, which can advance the current membrane technology for efficient H2 purification.

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