Computational screening of MOFs for CO2 capture

Name: Computational screening of MOFs for CO2 capture
Author: Altıntaş, Ç., Fındıkçı, İlknur Eruçar, Keskin, S.

Title	Computational screening of MOFs for CO2 capture
Author	Altıntaş, Ç., Fındıkçı, İlknur Eruçar, Keskin, S.
Publication Date:	2021-03-30
Publication Place	- Springer
Type	Book
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-3-030-63379-0
Record ID	aea0b86e-f71f-45ec-958f-b8815a912605
Library Location	Natural and Mathematical Sciences
Date	2021-03-30
Sample Text	The capture of CO2 (carbon dioxide) is an urgent environmental issue due to global warming. Adsorption-based CO2 capture using a new family of porous materials, metal-organic frameworks (MOFs), has been considered as a promising alternative to conventional methods. The rapid increase in the number of synthesized MOFs offers various materials for efficient CO2 capture, but assessing the performance of each MOF material using purely experimental methods is challenging. Recent progress in computational tools, high-throughput molecular simulations, and machine learning algorithms provide great opportunities for effective computational screening of MOFs with the aim of identifying the most promising adsorbents for CO2 capture prior to experimental studies. In this chapter, we focused on the recent advances in high-throughput screening of MOFs for CO2 capture and separation. We first reviewed the details of molecular simulation methods to compute CO2 adsorption properties of MOFs and adsorbent performance evaluation metrics that have been used to assess the CO2 separation potential of MOFs. Large-scale computational screening studies and quantitative structure-performance relationships obtained from molecular simulations were then discussed. Finally, opportunities and challenges of using computational tools to reveal the potential of MOFs for CO2 capture and separation were addressed.
DOI	10.1007/978-3-030-63380-6_6

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Özyeğin University

Computational screening of MOFs for CO2 capture

Author Altıntaş, Ç., Fındıkçı, İlknur Eruçar, Keskin, S.

Publication Date 2021-03-30

Publication Place - Springer

Type Book

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-3-030-63379-0

Record ID aea0b86e-f71f-45ec-958f-b8815a912605

Library Location Natural and Mathematical Sciences

Date 2021-03-30

Sample Text The capture of CO2 (carbon dioxide) is an urgent environmental issue due to global warming. Adsorption-based CO2 capture using a new family of porous materials, metal-organic frameworks (MOFs), has been considered as a promising alternative to conventional methods. The rapid increase in the number of synthesized MOFs offers various materials for efficient CO2 capture, but assessing the performance of each MOF material using purely experimental methods is challenging. Recent progress in computational tools, high-throughput molecular simulations, and machine learning algorithms provide great opportunities for effective computational screening of MOFs with the aim of identifying the most promising adsorbents for CO2 capture prior to experimental studies. In this chapter, we focused on the recent advances in high-throughput screening of MOFs for CO2 capture and separation. We first reviewed the details of molecular simulation methods to compute CO2 adsorption properties of MOFs and adsorbent performance evaluation metrics that have been used to assess the CO2 separation potential of MOFs. Large-scale computational screening studies and quantitative structure-performance relationships obtained from molecular simulations were then discussed. Finally, opportunities and challenges of using computational tools to reveal the potential of MOFs for CO2 capture and separation were addressed.

DOI 10.1007/978-3-030-63380-6_6