A Systematic Approach for Incorporating Structural Flexibility in High-Throughput Computational Screening of Metal-Organic Frameworks for Xylene Separation

被引:0
|
作者
Mohamed, Saad Aldin [1 ]
Zheng, Rui [1 ]
Zhu, Nengxiu [1 ]
Zhao, Dan [1 ]
Jiang, Jianwen [1 ]
机构
[1] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117576, Singapore
基金
新加坡国家研究基金会;
关键词
ZEOLITIC IMIDAZOLATE FRAMEWORK-8; FORCE-FIELD; ADSORPTION; ISOMERS; DYNAMICS;
D O I
10.1021/jacs.5c01749
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Separation of xylene isomers poses a significant challenge due to their similar physicochemical properties. Currently, zeolites are utilized as adsorbents for this purpose in the chemical industry despite suboptimal separation performance. With tunable pore size and chemical functionality, metal-organic frameworks (MOFs) are promising adsorbents for separation. By virtue of high-throughput computational screening (HTCS), the adsorption performance of a large collection of MOFs can be evaluated in silico by using Monte Carlo (MC) simulations. Unlike prior studies assuming rigid structures of MOFs during screening, we develop a systematic approach for incorporating flexibility in HTCS for xylene separation. First, MOFs are judiciously classified with external flexibility (volume/shape changes) and internal flexibility (intraframework fluctuations), respectively, based on the nature and extent of structural deformation from molecular dynamics (MD) simulations. Afterward, adsorption in MOFs with external flexibility is simulated by hybrid MC/MD method, the flexible snapshot method is used for MOFs with a sort of internal flexibility, and grand-canonical MC (GCMC) method is employed for MOFs with negligible flexibility. Finally, top-performing MOFs are identified for xylene separation. While substantially reducing computational cost, this study also delivers more reliable results compared to the assumption of rigid structures. The HTCS approach is versatile and can be extended beyond MOFs, offering a robust tool for the virtual screening of other soft-porous materials for a wide range of important applications.
引用
收藏
页码:12251 / 12262
页数:12
相关论文
共 50 条
  • [1] High-throughput computational screening of metal-organic frameworks
    Colon, Yamil J.
    Snurr, Randall Q.
    CHEMICAL SOCIETY REVIEWS, 2014, 43 (16) : 5735 - 5749
  • [2] Research Progress of High-throughput Computational Screening of Metal-Organic Frameworks
    Liu Zhilu
    Li Wei
    Liu Hao
    Zhuang Xudong
    Li Song
    ACTA CHIMICA SINICA, 2019, 77 (04) : 323 - 339
  • [3] High-Throughput Computational Screening of Metal-Organic Frameworks for Thiol Capture
    Qiao, Zhiwei
    Xu, Qisong
    Cheetham, Anthony K.
    Jiang, Jianwen
    JOURNAL OF PHYSICAL CHEMISTRY C, 2017, 121 (40): : 22208 - 22215
  • [4] High-Throughput Computational Screening of Metal-Organic Frameworks for the Separation of Methane from Ethane and Propane
    Ponraj, Yadava Krishnan
    Borah, Bhaskarjyoti
    JOURNAL OF PHYSICAL CHEMISTRY C, 2021, 125 (03): : 1839 - 1854
  • [5] Machine Learning Accelerated High-Throughput Computational Screening of Metal-Organic Frameworks
    Li, Wei
    Liang, Tiangui
    Lin, Yuanchuang
    Wu, Weixiong
    Li, Song
    PROGRESS IN CHEMISTRY, 2022, 34 (12) : 2619 - 2637
  • [6] High-throughput screening of metal-organic frameworks for kinetic separation of propane and propene
    Pramudya, Yohanes
    Bonakala, Satyanarayana
    Antypov, Dmytro
    Bhatt, Prashant M.
    Shkurenko, Aleksander
    Eddaoudi, Mohamed
    Rosseinsky, Matthew J.
    Dyer, Matthew S.
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2020, 22 (40) : 23073 - 23082
  • [7] High-Throughput Screening of Metal-Organic Frameworks for CO2 Separation
    Han, Sangil
    Huang, Yougui
    Watanabe, Taku
    Dai, Ying
    Walton, Krista S.
    Nair, Sankar
    Sholl, David S.
    Meredith, J. Carson
    ACS COMBINATORIAL SCIENCE, 2012, 14 (04) : 263 - 267
  • [8] Metal-Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning
    Quo, Zhiwei
    Yan, Yaling
    Tang, Yaxing
    Liang, Hong
    Jiang, Jianwen
    JOURNAL OF PHYSICAL CHEMISTRY C, 2021, 125 (14): : 7839 - 7848
  • [9] High-throughput screening of metal-organic frameworks for hydrogen purification
    Wang, Shihui
    Cheng, Min
    Luo, Lei
    Ji, Xu
    Liu, Chong
    Bi, Kexin
    Zhou, Li
    CHEMICAL ENGINEERING JOURNAL, 2023, 451
  • [10] High-Throughput Computational Screening of Metal−Organic Frameworks for the Separation of Methane from Ethane and Propane
    Ponraj, Yadava Krishnan
    Borah, Bhaskarjyoti
    Journal of Physical Chemistry C, 2021, 125 (03): : 1839 - 1854