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Kurisingal JF, Yun H, Hong CS. Porous organic materials for iodine adsorption. JOURNAL OF HAZARDOUS MATERIALS 2023; 458:131835. [PMID: 37348374 DOI: 10.1016/j.jhazmat.2023.131835] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/23/2023] [Accepted: 06/10/2023] [Indexed: 06/24/2023]
Abstract
The nuclear industry will continue to develop rapidly and produce energy in the foreseeable future; however, it presents unique challenges regarding the disposal of released waste radionuclides because of their volatility and long half-life. The release of radioactive isotopes of iodine from uranium fission reactions is a challenge. Although various adsorbents have been explored for the uptake of iodine, there is still interest in novel adsorbents. The novel adsorbents should be synthesized using reliable and economically feasible synthetic procedures. Herein, we discussed the state-of-the-art performance of various categories of porous organic materials including covalent organic frameworks, covalent triazine frameworks, porous aromatic frameworks, porous organic cages, among other porous organic polymers for the uptake of iodine. This review discussed the synthesis of porous organic materials and their iodine adsorption capacity and reusability. Finally, the challenges and prospects for iodine capture using porous organic materials are highlighted.
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Affiliation(s)
| | - Hongryeol Yun
- Department of Chemistry, Korea University, Seoul 02841, Republic of Korea
| | - Chang Seop Hong
- Department of Chemistry, Korea University, Seoul 02841, Republic of Korea.
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2
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Li A, Bueno-Perez R, Fairen-Jimenez D. Identifying porous cage subsets in the Cambridge Structural Database using topological data analysis. Chem Sci 2022; 13:13507-13523. [PMID: 36507160 PMCID: PMC9682994 DOI: 10.1039/d2sc03171j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022] Open
Abstract
As rationally designable materials, the variety and number of synthesised metal-organic cages (MOCs) and organic cages (OCs) are expected to grow in the Cambridge Structural Database (CSD). In this regard, two of the most important questions are, which structures are already present in the CSD and how can they be identified? Here, we present a cage mining methodology based on topological data analysis and a combination of supervised and unsupervised learning that led to the derivation of - to the best of our knowledge - the first and only MOC dataset of 1839 structures and the largest experimental OC dataset of 7736 cages, as of March 2022. We illustrate the use of such datasets with a high-throughput screening of MOCs and OCs for xenon/krypton separation, important gases in multiple industries, including healthcare.
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Affiliation(s)
- Aurelia Li
- The Adsorption & Advanced Materials Laboratory (AML), Department of Chemical Engineering & Biotechnology, University of CambridgePhilippa Fawcett DriveCambridge CB3 0ASUK
| | - Rocio Bueno-Perez
- The Adsorption & Advanced Materials Laboratory (AML), Department of Chemical Engineering & Biotechnology, University of CambridgePhilippa Fawcett DriveCambridge CB3 0ASUK
| | - David Fairen-Jimenez
- The Adsorption & Advanced Materials Laboratory (AML), Department of Chemical Engineering & Biotechnology, University of CambridgePhilippa Fawcett DriveCambridge CB3 0ASUK
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3
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He A, Jiang Z, Wu Y, Hussain H, Rawle J, Briggs ME, Little MA, Livingston AG, Cooper AI. A smart and responsive crystalline porous organic cage membrane with switchable pore apertures for graded molecular sieving. NATURE MATERIALS 2022; 21:463-470. [PMID: 35013552 PMCID: PMC8971131 DOI: 10.1038/s41563-021-01168-z] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/11/2021] [Indexed: 05/06/2023]
Abstract
Membranes with high selectivity offer an attractive route to molecular separations, where technologies such as distillation and chromatography are energy intensive. However, it remains challenging to fine tune the structure and porosity in membranes, particularly to separate molecules of similar size. Here, we report a process for producing composite membranes that comprise crystalline porous organic cage films fabricated by interfacial synthesis on a polyacrylonitrile support. These membranes exhibit ultrafast solvent permeance and high rejection of organic dyes with molecular weights over 600 g mol-1. The crystalline cage film is dynamic, and its pore aperture can be switched in methanol to generate larger pores that provide increased methanol permeance and higher molecular weight cut-offs (1,400 g mol-1). By varying the water/methanol ratio, the film can be switched between two phases that have different selectivities, such that a single, 'smart' crystalline membrane can perform graded molecular sieving. We exemplify this by separating three organic dyes in a single-stage, single-membrane process.
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Affiliation(s)
- Ai He
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
| | - Zhiwei Jiang
- Department of Chemical Engineering, Imperial College London, South Kensington, London, UK
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Yue Wu
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
| | | | | | - Michael E Briggs
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
| | - Marc A Little
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK
| | - Andrew G Livingston
- Department of Chemical Engineering, Imperial College London, South Kensington, London, UK.
- School of Engineering and Materials Science, Queen Mary University of London, London, UK.
| | - Andrew I Cooper
- Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK.
- Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK.
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4
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Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption. ADSORPT SCI TECHNOL 2022. [DOI: 10.1155/2022/3901608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater.
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5
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Mukherjee K, Colón YJ. Machine learning and descriptor selection for the computational discovery of metal-organic frameworks. MOLECULAR SIMULATION 2021. [DOI: 10.1080/08927022.2021.1916014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Krishnendu Mukherjee
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Yamil J. Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
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6
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Deegan MM, Dworzak MR, Gosselin AJ, Korman KJ, Bloch ED. Gas Storage in Porous Molecular Materials. Chemistry 2021; 27:4531-4547. [PMID: 33112484 DOI: 10.1002/chem.202003864] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/25/2020] [Indexed: 02/06/2023]
Abstract
Molecules with permanent porosity in the solid state have been studied for decades. Porosity in these systems is governed by intrinsic pore space, as in cages or macrocycles, and extrinsic void space, created through loose, intermolecular solid-state packing. The development of permanently porous molecular materials, especially cages with organic or metal-organic composition, has seen increased interest over the past decade, and as such, incredibly high surface areas have been reported for these solids. Despite this, examples of these materials being explored for gas storage applications are relatively limited. This minireview outlines existing molecular systems that have been investigated for gas storage and highlights strategies that have been used to understand adsorption mechanisms in porous molecular materials.
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Affiliation(s)
- Meaghan M Deegan
- Department of Chemistry & Biochemistry, University of Delaware, Newark, DE, 19716, USA
| | - Michael R Dworzak
- Department of Chemistry & Biochemistry, University of Delaware, Newark, DE, 19716, USA
| | - Aeri J Gosselin
- Department of Chemistry & Biochemistry, University of Delaware, Newark, DE, 19716, USA
| | - Kyle J Korman
- Department of Chemistry & Biochemistry, University of Delaware, Newark, DE, 19716, USA
| | - Eric D Bloch
- Department of Chemistry & Biochemistry, University of Delaware, Newark, DE, 19716, USA
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Greenaway RL, Jelfs KE. Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2004831. [PMID: 33565203 DOI: 10.1002/adma.202004831] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/28/2020] [Indexed: 06/12/2023]
Abstract
Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.
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Affiliation(s)
- Rebecca L Greenaway
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, W12 0BZ, UK
| | - Kim E Jelfs
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, W12 0BZ, UK
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Townsend J, Micucci CP, Hymel JH, Maroulas V, Vogiatzis KD. Representation of molecular structures with persistent homology for machine learning applications in chemistry. Nat Commun 2020; 11:3230. [PMID: 32591514 PMCID: PMC7319956 DOI: 10.1038/s41467-020-17035-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 05/28/2020] [Indexed: 11/27/2022] Open
Abstract
Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.
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Affiliation(s)
- Jacob Townsend
- Department of Chemistry, University of Tennessee, Knoxville, TN, 37996-1600, USA
| | | | - John H Hymel
- Department of Chemistry, University of Tennessee, Knoxville, TN, 37996-1600, USA
| | - Vasileios Maroulas
- Department of Mathematics, University of Tennessee, Knoxville, TN, 37996-1320, USA.
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Young TA, Gheorghe R, Duarte F. cgbind: A Python Module and Web App for Automated Metallocage Construction and Host–Guest Characterization. J Chem Inf Model 2020; 60:3546-3557. [DOI: 10.1021/acs.jcim.0c00519] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Tom A. Young
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Razvan Gheorghe
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Fernanda Duarte
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
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10
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Moosavi SM, Xu H, Chen L, Cooper AI, Smit B. Geometric landscapes for material discovery within energy-structure-function maps. Chem Sci 2020; 11:5423-5433. [PMID: 34094069 PMCID: PMC8159328 DOI: 10.1039/d0sc00049c] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 04/28/2020] [Indexed: 01/12/2023] Open
Abstract
Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1000s to 10 000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.
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Affiliation(s)
- Seyed Mohamad Moosavi
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL) Rue de l'Industrie 17 CH-1951 Sion Valais Switzerland
| | - Henglu Xu
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL) Rue de l'Industrie 17 CH-1951 Sion Valais Switzerland
| | - Linjiang Chen
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, Department of Chemistry, University of Liverpool 51 Oxford Street Liverpool L7 3NY UK
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory, Department of Chemistry, University of Liverpool 51 Oxford Street Liverpool L7 3NY UK
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL) Rue de l'Industrie 17 CH-1951 Sion Valais Switzerland
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11
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Kravchenko O, Varava A, Pokorny FT, Devaurs D, Kavraki LE, Kragic D. A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction. J Chem Inf Model 2020; 60:1302-1316. [PMID: 32130862 PMCID: PMC7307881 DOI: 10.1021/acs.jcim.9b00945] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
![]()
We define a molecular caging complex as a pair
of molecules in which one molecule (the “host” or “cage”)
possesses a cavity that can encapsulate the other molecule (the “guest”)
and prevent it from escaping. Molecular caging complexes can be useful
in applications such as molecular shape sorting, drug delivery, and
molecular immobilization in materials science, to name just a few.
However, the design and computational discovery of new caging complexes
is a challenging task, as it is hard to predict whether one molecule
can encapsulate another because their shapes can be quite complex.
In this paper, we propose a computational screening method that predicts
whether a given pair of molecules form a caging complex. Our method
is based on a caging verification algorithm that was designed by our
group for applications in robotic manipulation. We tested our algorithm
on three pairs of molecules that were previously described in a pioneering
work on molecular caging complexes and found that our results are
fully consistent with the previously reported ones. Furthermore, we
performed a screening experiment on a data set consisting of 46 hosts
and four guests and used our algorithm to predict which pairs are
likely to form caging complexes. Our method is computationally efficient
and can be integrated into a screening pipeline to complement experimental
techniques.
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Affiliation(s)
- Oleksandr Kravchenko
- Department of Chemistry, School of Engineering Sciences in Chemistry, Biology and Health (CBH), KTH Royal Institute of Technology, 11428 Stockholm, Sweden
| | - Anastasiia Varava
- Division of Robotics, Perception and Learning (RPL), School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, Sweden
| | - Florian T Pokorny
- Division of Robotics, Perception and Learning (RPL), School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, Sweden
| | - Didier Devaurs
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP (Institute of Engineering, Université Grenoble Alpes), LJK, 38000 Grenoble, France
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Danica Kragic
- Division of Robotics, Perception and Learning (RPL), School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, Sweden
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Fraux G, Chibani S, Coudert FX. Modelling of framework materials at multiple scales: current practices and open questions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180220. [PMID: 31130101 PMCID: PMC6562347 DOI: 10.1098/rsta.2018.0220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties. This article is part of the theme issue 'Mineralomimesis: natural and synthetic frameworks in science and technology'.
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Sturluson A, Huynh MT, Kaija AR, Laird C, Yoon S, Hou F, Feng Z, Wilmer CE, Colón YJ, Chung YG, Siderius DW, Simon CM. The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation. MOLECULAR SIMULATION 2019; 45:10.1080/08927022.2019.1648809. [PMID: 31579352 PMCID: PMC6774364 DOI: 10.1080/08927022.2019.1648809] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 07/15/2019] [Indexed: 01/10/2023]
Abstract
Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.
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Affiliation(s)
- Arni Sturluson
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Melanie T. Huynh
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Alec R. Kaija
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Caleb Laird
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Sunghyun Yoon
- School of Chemical and Biomolecular Engineering, Pusan National University, Busan, Korea (South)
| | - Feier Hou
- Western Oregon University. Department of Chemistry, Monmouth, OR, USA
| | - Zhenxing Feng
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Christopher E. Wilmer
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yamil J. Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Yongchul G. Chung
- School of Chemical and Biomolecular Engineering, Pusan National University, Busan, Korea (South)
| | - Daniel W. Siderius
- Chemical Sciences Division, National Institute of Standards and Technology. Gaithersburg, MD, USA
| | - Cory M. Simon
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
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