Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning.
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ABSTRACT: A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH4/H2 separation. We studied 597 synthesized COFs for adsorption of a CH4/H2 mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH4/H2 selectivities, CH4 working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was
SUBMITTER: Aksu GO
PROVIDER: S-EPMC10335334 | biostudies-literature | 2023 Jul
REPOSITORIES: biostudies-literature
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