High-Throughput Computational Screening of Metal-Organic Frameworks for the Separation of Methane from Ethane and Propane

Efficient separation of mixtures of light hydrocarbons is an industrially demanding but challenging process. In this study, we present a high-throughput computational screening of similar to 12,000 experimentally realizable metal-organic framework (MOF) structures in order to identify the best candidate that can separate methane from ethane and propane at ambient conditions. We calculated several performance metrics-adsorption selectivity, working capacity, and regenerability to assess the performance of the MOFs in the database. The MOFs were screened based on high adsorbent performance score and regenerability >80%. MOFs AZIVAI and BEWCUD were found to be performing the best for the separation of methane from its binary and ternary mixtures with ethane and propane. We looked at various structure-property correlations of selectivity and working capacity that reveal a generic trade-off relation between these two metrics. Selectivity correlates strongly with the heat of adsorption in a linear fashion, whereas working capacity exhibits an increasing and then decreasing behavior with the heat of adsorption complementing the trade-off relation between selectivity and working capacity. We have also screened out few promising MOFs that are thermally and chemically stable and discussed their experimental stability conditions in detail.