Computational Screening of Metal-Organic Frameworks for Xenon/Krypton Separation

被引：154
|
作者：
Ryan, Patrick [1 ]
Farha, Omar K. [2 ]
Broadbelt, Linda J. [1 ]
Snurr, Randall Q. [1 ]
机构：
[1] Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL 60208 USA
[2] Northwestern Univ, Dept Chem, Evanston, IL 60208 USA
来源：
AICHE JOURNAL | 2011年 / 57卷 / 07期
关键词：
adsorption/gas; simulation; molecular; thermodynamics/statistical; MONTE-CARLO SIMULATIONS; MOLECULAR SIMULATION; HYDROGEN STORAGE; ADSORPTION SELECTIVITY; POROUS MATERIALS; METHANE STORAGE; FORCE-FIELD; PORE-SIZE; SITES; CO2;
D O I：
10.1002/aic.12397
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
A variety of metal-organic frameworks (MOFs) with varying linkers, topologies, pore sizes, and metal atoms were screened for xenon/krypton separation using grand canonical Monte Carlo (GCMC) simulations. The results indicate that small pores with strong adsorption sites are desired to preferentially adsorb xenon over krypton in multicomponent adsorption. However, if the pore size is too small, it can significantly limit overall gas uptake, which is undesirable. Based on our simulations, MOF-505 was identified as a promising material due to its increased xenon selectivity over a wider pressure range compared with other MOFs investigated. (C) 2010 American Institute of Chemical Engineers AIChE J, 57: 1759-1766, 2011
引用
收藏
页码：1759 / 1766
页数：8
相关论文
共 50 条
  • [21] Modeling gas separation in metal-organic frameworks
    Wells, Brad A.
    Chaffee, Alan L.
    ADSORPTION-JOURNAL OF THE INTERNATIONAL ADSORPTION SOCIETY, 2011, 17 (01): : 255 - 264
  • [22] In silico screening of metal-organic frameworks in separation applications
    Krishna, Rajamani
    van Baten, Jasper M.
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2011, 13 (22) : 10593 - 10616
  • [23] Separation of Xenon and Krypton in the Metal-Organic Frameworks M2(m-dobdc) (M=Co, Ni)
    Kapelewski, Matthew T.
    Oktawiec, Julia
    Runcevski, Tomce
    Gonzalez, Miguel I.
    Long, Jeffrey R.
    ISRAEL JOURNAL OF CHEMISTRY, 2018, 58 (9-10) : 1138 - 1143
  • [24] 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
  • [25] In Silico Screening of Metal-Organic Frameworks and Zeolites for He/N2 Separation
    Grenev, Ivan V.
    Gavrilov, Vladimir Yu.
    MOLECULES, 2023, 28 (01):
  • [26] Quantum Sieving in Metal-Organic Frameworks: A Computational Study
    Liu, Dahuan
    Wang, Wenjie
    Mi, Jianguo
    Zhong, Chongli
    Yang, Qingyuan
    Wu, Dong
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (01) : 434 - 442
  • [27] Understanding gas separation in metal-organic frameworks using computer modeling
    Liu, Dahuan
    Zhong, Chongli
    JOURNAL OF MATERIALS CHEMISTRY, 2010, 20 (46) : 10308 - 10318
  • [28] Machine-learning-assisted high-throughput computational screening of high performance metal-organic frameworks
    Shi, Zenan
    Yang, Wenyuan
    Deng, Xiaomei
    Cai, Chengzhi
    Yan, Yaling
    Liang, Hong
    Liu, Zili
    Qiao, Zhiwei
    MOLECULAR SYSTEMS DESIGN & ENGINEERING, 2020, 5 (04) : 725 - 742
  • [29] Computational Screening of Metal Catecholates for Ammonia Capture in Metal-Organic Frameworks
    Kim, Ki Chul
    Moghadam, Peyman Z.
    Fairen-Jimenez, David
    Snurr, Randall Q.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2015, 54 (13) : 3257 - 3267
  • [30] Metal-Organic Frameworks for Separation
    Zhao, Xiang
    Wang, Yanxiang
    Li, Dong-Sheng
    Bu, Xianhui
    Feng, Pingyun
    ADVANCED MATERIALS, 2018, 30 (37)
12345