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Title: Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules

Although computational models have been used to predict adsorption of molecules in large libraries of porous adsorbents, previous work of this kind has focused on a small number of molecules as potential adsorbates. In this paper we use molecular simulations to consider the adsorption of a diverse range of molecules in a large collection of metal-organic framework (MOF) materials. Specifically, we obtain 11,304 isotherms from molecular simulations of 24 different adsorbates in 471 MOFs. This information allows us to explore several interesting questions that could not be addressed with previously available data. Here, we introduce highly computationally efficient methods that can predict isotherms for a wide range of adsorbing molecules with far less computation than traditional molecular simulations. By characterizing the 276 binary mixtures defined by the molecules we considered, we show that “privileged” adsorbents exist that are effective for separating many different molecular mixtures. Finally, we show that correlations that have been developed previously to predict molecular solubility in polymers are surprisingly effective in predicting the average properties of molecules adsorbing in MOFs.
Authors:
 [1] ;  [2] ;  [1] ; ORCiD logo [1]
  1. Georgia Inst. of Technology, Atlanta, GA (United States). School of Chemical & Biomolecular Engineering
  2. Georgia Inst. of Technology, Atlanta, GA (United States). School of Chemical & Biomolecular Engineering; South China Univ. of Technology (SCUT), Guangzhou (China). School of Chemistry and Chemical Engineering
Publication Date:
Grant/Contract Number:
SC0008688; AC02-06CH11357; 1604375
Type:
Accepted Manuscript
Journal Name:
ChemSusChem
Additional Journal Information:
Journal Volume: 11; Journal Issue: 9; Journal ID: ISSN 1864-5631
Publisher:
ChemPubSoc Europe
Research Org:
Univ. of Minnesota, Minneapolis, MN (United States). Nanoporous Materials Genome Center
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division; National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; adsorption; high-throughput screening; metal–organic frameworks; molecular modeling; separations
OSTI Identifier:
1460998

Tang, Dai, Wu, Ying, Verploegh, Ross J., and Sholl, David S.. Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules. United States: N. p., Web. doi:10.1002/cssc.201702289.
Tang, Dai, Wu, Ying, Verploegh, Ross J., & Sholl, David S.. Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules. United States. doi:10.1002/cssc.201702289.
Tang, Dai, Wu, Ying, Verploegh, Ross J., and Sholl, David S.. 2018. "Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules". United States. doi:10.1002/cssc.201702289.
@article{osti_1460998,
title = {Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules},
author = {Tang, Dai and Wu, Ying and Verploegh, Ross J. and Sholl, David S.},
abstractNote = {Although computational models have been used to predict adsorption of molecules in large libraries of porous adsorbents, previous work of this kind has focused on a small number of molecules as potential adsorbates. In this paper we use molecular simulations to consider the adsorption of a diverse range of molecules in a large collection of metal-organic framework (MOF) materials. Specifically, we obtain 11,304 isotherms from molecular simulations of 24 different adsorbates in 471 MOFs. This information allows us to explore several interesting questions that could not be addressed with previously available data. Here, we introduce highly computationally efficient methods that can predict isotherms for a wide range of adsorbing molecules with far less computation than traditional molecular simulations. By characterizing the 276 binary mixtures defined by the molecules we considered, we show that “privileged” adsorbents exist that are effective for separating many different molecular mixtures. Finally, we show that correlations that have been developed previously to predict molecular solubility in polymers are surprisingly effective in predicting the average properties of molecules adsorbing in MOFs.},
doi = {10.1002/cssc.201702289},
journal = {ChemSusChem},
number = 9,
volume = 11,
place = {United States},
year = {2018},
month = {4}
}

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