Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas
To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO<sub>2</sub> from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this wor...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Membranes |
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| Online Access: | https://www.mdpi.com/2077-0375/12/7/700 |
| _version_ | 1797417079599529984 |
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| author | Yizhen Situ Xueying Yuan Xiangning Bai Shuhua Li Hong Liang Xin Zhu Bangfen Wang Zhiwei Qiao |
| author_facet | Yizhen Situ Xueying Yuan Xiangning Bai Shuhua Li Hong Liang Xin Zhu Bangfen Wang Zhiwei Qiao |
| author_sort | Yizhen Situ |
| collection | DOAJ |
| description | To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO<sub>2</sub> from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO<sub>2</sub>, N<sub>2</sub>, and O<sub>2</sub>) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas. |
| first_indexed | 2024-03-09T06:13:43Z |
| format | Article |
| id | doaj.art-6e0fc78bd12b4582b112a00c5c9903e0 |
| institution | Directory Open Access Journal |
| issn | 2077-0375 |
| language | English |
| last_indexed | 2024-03-09T06:13:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Membranes |
| spelling | doaj.art-6e0fc78bd12b4582b112a00c5c9903e02023-12-03T11:55:30ZengMDPI AGMembranes2077-03752022-07-0112770010.3390/membranes12070700Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue GasYizhen Situ0Xueying Yuan1Xiangning Bai2Shuhua Li3Hong Liang4Xin Zhu5Bangfen Wang6Zhiwei Qiao7Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, ChinaTo combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO<sub>2</sub> from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO<sub>2</sub>, N<sub>2</sub>, and O<sub>2</sub>) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.https://www.mdpi.com/2077-0375/12/7/700membrane separationmetal–organic frameworksmachine learning |
| spellingShingle | Yizhen Situ Xueying Yuan Xiangning Bai Shuhua Li Hong Liang Xin Zhu Bangfen Wang Zhiwei Qiao Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas Membranes membrane separation metal–organic frameworks machine learning |
| title | Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas |
| title_full | Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas |
| title_fullStr | Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas |
| title_full_unstemmed | Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas |
| title_short | Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO<sub>2</sub> from Flue Gas |
| title_sort | large scale screening and machine learning for metal organic framework membranes to capture co sub 2 sub from flue gas |
| topic | membrane separation metal–organic frameworks machine learning |
| url | https://www.mdpi.com/2077-0375/12/7/700 |
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