Literature DB >> 23654217

Large-scale screening of zeolite structures for CO2 membrane separations.

Jihan Kim1, Mahmoud Abouelnasr, Li-Chiang Lin, Berend Smit.   

Abstract

We have conducted large-scale screening of zeolite materials for CO2/CH4 and CO2/N2 membrane separation applications using the free energy landscape of the guest molecules inside these porous materials. We show how advanced molecular simulations can be integrated with the design of a simple separation process to arrive at a metric to rank performance of over 87,000 different zeolite structures, including the known IZA zeolite structures. Our novel, efficient algorithm using graphics processing units can accurately characterize both the adsorption and diffusion properties of a given structure in just a few seconds and accordingly find a set of optimal structures for different desired purity of separated gases from a large database of porous materials in reasonable wall time. Our analysis reveals that the optimal structures for separations usually consist of channels with adsorption sites spread relatively uniformly across the entire channel such that they feature well-balanced CO2 adsorption and diffusion properties. Our screening also shows that the top structures in the predicted zeolite database outperform the best known zeolite by a factor of 4-7. Finally, we have identified a completely different optimal set of zeolite structures that are suitable for an inverse process, in which the CO2 is retained while CH4 or N2 is passed through a membrane.

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Year:  2013        PMID: 23654217     DOI: 10.1021/ja400267g

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  9 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

2.  Assessing the stability of Pd-exchanged sites in zeolites with the aid of a high throughput quantum chemistry workflow.

Authors:  Hassan A Aljama; Martin Head-Gordon; Alexis T Bell
Journal:  Nat Commun       Date:  2022-05-25       Impact factor: 17.694

3.  In silico prediction and screening of modular crystal structures via a high-throughput genomic approach.

Authors:  Yi Li; Xu Li; Jiancong Liu; Fangzheng Duan; Jihong Yu
Journal:  Nat Commun       Date:  2015-09-23       Impact factor: 14.919

4.  Polarizable Force Fields for CO2 and CH4 Adsorption in M-MOF-74.

Authors:  Tim M Becker; Jurn Heinen; David Dubbeldam; Li-Chiang Lin; Thijs J H Vlugt
Journal:  J Phys Chem C Nanomater Interfaces       Date:  2017-01-31       Impact factor: 4.126

5.  Representation of molecular structures with persistent homology for machine learning applications in chemistry.

Authors:  Jacob Townsend; Cassie Putman Micucci; John H Hymel; Vasileios Maroulas; Konstantinos D Vogiatzis
Journal:  Nat Commun       Date:  2020-06-26       Impact factor: 14.919

6.  Computational Screening of Metal-Organic Frameworks for Membrane-Based CO2/N2/H2O Separations: Best Materials for Flue Gas Separation.

Authors:  Hilal Daglar; Seda Keskin
Journal:  J Phys Chem C Nanomater Interfaces       Date:  2018-07-03       Impact factor: 4.126

7.  Bridging scales in disordered porous media by mapping molecular dynamics onto intermittent Brownian motion.

Authors:  Colin Bousige; Pierre Levitz; Benoit Coasne
Journal:  Nat Commun       Date:  2021-02-15       Impact factor: 14.919

8.  Performance-Based Screening of Porous Materials for Carbon Capture.

Authors:  Amir H Farmahini; Shreenath Krishnamurthy; Daniel Friedrich; Stefano Brandani; Lev Sarkisov
Journal:  Chem Rev       Date:  2021-08-10       Impact factor: 60.622

9.  Molecular Simulations of MOF Membranes and Performance Predictions of MOF/Polymer Mixed Matrix Membranes for CO2/CH4 Separations.

Authors:  Cigdem Altintas; Seda Keskin
Journal:  ACS Sustain Chem Eng       Date:  2018-12-18       Impact factor: 8.198

  9 in total

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