Hypothetical yet effective: Computational identification of high-performing MOFs for CO2 capture. (April 2022)
- Record Type:
- Journal Article
- Title:
- Hypothetical yet effective: Computational identification of high-performing MOFs for CO2 capture. (April 2022)
- Main Title:
- Hypothetical yet effective: Computational identification of high-performing MOFs for CO2 capture
- Authors:
- Demir, Hakan
Keskin, Seda - Abstract:
- Highlights: CO2 /CO, CO2 /H2, and CO2 /N2 separations are investigated using anion-pillared MOFs. Adsorption selectivity, working capacity, and regenerability of MOFs are calculated. The top MOFs for each separation are identified. Stronger adsorbing gas is closer to the anions of MOF than the weaker adsorbing gas. Abstract: With the advances in computational resources and algorithms, computer simulations are being increasingly used to tackle the most challenging problems of the world. Among them, CO2 capture is a topic that needs imminent attention as the presence of high levels of CO2 in the air can lead to drastic shifts in global climate. Here, a recently developed hypothetical metal-organic framework (MOF) database comprised of anion-pillared (AP) MOFs is computationally screened for the separation of CO2 /CO, CO2 /H2, and CO2 /N2 gas mixtures at room temperature. The best performing MOFs are identified using three performance metrics, adsorption selectivity, working capacity, and regenerability, in conjunction. In these top materials, the preferential adsorption sites are illustrated, which will be useful in guiding the experimental design of new MOFs with extraordinarily high CO2 selectivities. The favorable separation performances of AP MOFs suggest that efficient gas separations can be conducted using MOFs without open metal sites. Graphical abstract: Image, graphical abstract
- Is Part Of:
- Computers & chemical engineering. Volume 160(2022)
- Journal:
- Computers & chemical engineering
- Issue:
- Volume 160(2022)
- Issue Display:
- Volume 160, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 160
- Issue:
- 2022
- Issue Sort Value:
- 2022-0160-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-04
- Subjects:
- Chemical engineering -- Data processing -- Periodicals
660.0285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00981354 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compchemeng.2022.107705 ↗
- Languages:
- English
- ISSNs:
- 0098-1354
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.664000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21035.xml