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 (C 4 H 10 ) capture from air. Configurational Bias Monte Carlo (CBMC) simulations were used to study the adsorption of a quaternary gas mixture of N 2 , O 2 , Ar, and C 4 H 10 in QMOFs for two different processes, pressure swing adsorption (PSA) and vacuum-swing adsorption (VSA). Several adsorbent performance evaluation metrics, such as C 4 H 10 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 C 4 H 10 capture from a more realistic seven-component air mixture consisting of N 2 , O 2 , Ar, C 4 H 10 , C 3 H 8 , C 3 H 6 , and C 2 H 6 . Results showed that the top five QMOFs have C 4 H 10 selectivities between 6.3 × 10 3 and 9 × 10 3 (3.8 × 10 3 and 5 × 10 3 ) 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 C 4 H 10 selectivities. Radial distribution function analyses of the top materials revealed that C 4 H 10 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 C 4 H 10 capture from air.

Machine Learning Assisted Discovery of Efficient MOFs for One-Step C2H4 Purification from Ternary C2H2/C2H4/C2H6 Mixtures

“…The hMOF database is constructed using a recursion-based assembly algorithm with a library of typically metal clusters and organic linkers (102 SBUs), while the GMOF database is constructed using a quasi-reactive assembly algorithm (QReaxAA) with 17 metal clusters, 32 organic linkers, and 9 functional groups. Previous studies have already demonstrated promising applications of these two representative in-silico MOF databases in gas separation. − …”