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Large-Scale Computational Screening-Aided Development of High-Performance Adsorbent for Simultaneous Capture of Aromatic Volatile Organic Compounds.

Seo-Yul KimMin Woo ShinKwang Hyun OhYoun-Sang Bae
Published in: ACS applied materials & interfaces (2024)
The development of an efficient adsorbent for the simultaneous capture of large amounts of benzene, toluene, ethylbenzene, and xylene isomers (BTEX) is an important and challenging issue. Here, through a stepwise screening of 10,142 metal-organic framework (MOF) structures from the computation-ready, experimental (CoRE) MOF database, 65 MOFs are proposed as promising adsorbent candidates for BTEX capture by considering the structures with accessible pore sizes for BTEX adsorption, sufficient hydrophobicity, high benzene selectivity (>0.2), and large total BTEX uptake (>3 mmol/g). Among the top-performing MOFs in terms of the BTEX matrix (total BTEX uptake × benzene selectivity), EGUELUY01 was synthesized, and it exhibited large uptakes (≈5 mmol/g) for all BTEX components at concentrations of 1200-1500 ppm, which are superior to the BTEX uptake of the benchmark adsorbent, activated carbon. Moreover, some structure-property relationships required for BTEX adsorbents are provided through the obtained large-scale simulation data and machine learning analysis. The determined relationships will be useful for the future development of efficient BTEX adsorbents.
Keyphrases
  • metal organic framework
  • machine learning
  • aqueous solution
  • emergency department
  • big data
  • deep learning
  • mass spectrometry
  • artificial intelligence
  • current status
  • simultaneous determination
  • data analysis