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大规模计算筛选辅助开发用于同时捕获芳香族挥发性有机化合物的高性能吸附剂

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

作者信息

Kim Seo-Yul, Shin Min Woo, Oh Kwang Hyun, Bae Youn-Sang

机构信息

Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea.

School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

出版信息

ACS Appl Mater Interfaces. 2024 Aug 21;16(33):43565-43573. doi: 10.1021/acsami.4c08171. Epub 2024 Aug 12.

Abstract

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 (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.

摘要

开发一种能够同时高效捕获大量苯、甲苯、乙苯和二甲苯异构体(BTEX)的吸附剂是一个重要且具有挑战性的问题。在此,通过从计算就绪的实验(CoRE)金属有机框架(MOF)数据库中对10142种MOF结构进行逐步筛选,考虑到具有可用于BTEX吸附的孔径、足够的疏水性、高苯选择性(>0.2)和大的总BTEX吸附量(>3 mmol/g)的结构,提出了65种MOF作为有前景的BTEX捕获吸附剂候选物。在BTEX(总BTEX吸附量×苯选择性)方面表现最佳的MOF中,合成了EGUELUY01,它在1200 - 1500 ppm浓度下对所有BTEX组分都表现出较大的吸附量(≈5 mmol/g),优于基准吸附剂活性炭对BTEX的吸附量。此外,通过获得的大规模模拟数据和机器学习分析,提供了BTEX吸附剂所需的一些结构 - 性能关系。所确定的关系将有助于高效BTEX吸附剂的未来开发。

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