Hierarchical Computational Screening of Quantum Metal–Organic Framework Database to Identify Metal–Organic Frameworks for Volatile Organic-Compound Capture from Air

Abstract

The design and discovery of novel porous materials that can efficiently capture volatile organic compounds (VOCs) from air are critical to address one of the most important challenges of our world, air pollution. In this work, we studied a recently introduced metal–organic framework (MOF) database, namely, quantum MOF (QMOF) database, to unlock the potential of both experimentally synthesized and hypothetically generated structures for adsorption-based n-butane (C₄H₁₀) capture from air. Configurational Bias Monte Carlo (CBMC) simulations were used to study the adsorption of a quaternary gas mixture of N₂, O₂, Ar, and C₄H₁₀ in QMOFs for two different processes, pressure swing adsorption (PSA) and vacuum-swing adsorption (VSA). Several adsorbent performance evaluation metrics, such as C₄H₁₀ selectivity, working capacity, the adsorbent performance score, and percent regenerability, were used to identify the best adsorbent candidates, which were then further studied by molecular simulations for C₄H₁₀ capture from a more realistic seven-component air mixture consisting of N₂, O₂, Ar, C₄H₁₀, C₃H₈, C₃H₆, and C₂H₆. Results showed that the top five QMOFs have C₄H₁₀ selectivities between 6.3 × 10³ and 9 × 10³ (3.8 × 10³ and 5 × 10³) at 1 bar (10 bar). Detailed analysis of the structure–performance relations showed that low/mediocre porosity (0.4–0.6) and narrow pore sizes (6–9 Å) of QMOFs lead to high C₄H₁₀ selectivities. Radial distribution function analyses of the top materials revealed that C₄H₁₀ molecules tend to confine close to the organic parts of MOFs. Our results provided the first information in the literature about the VOC capture potential of a large variety and number of MOFs, which will be useful to direct the experimental efforts to the most promising adsorbent materials for C₄H₁₀ capture from air.