Machine learning-assisted development of gas separation membranes: A review
Gas separation membranes have been a hot topic of research in recent decades due to their low costs, high energy efficiency and wide range of applications. Machine learning provide a fast way to design gas separation membranes with required performance. This review systematically describes the proce...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Carbon Capture Science & Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772656825000144 |
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| author | An Li Jianchun Chu Shaoxuan Huang Yongqi Liu Maogang He Xiangyang Liu |
| author_facet | An Li Jianchun Chu Shaoxuan Huang Yongqi Liu Maogang He Xiangyang Liu |
| author_sort | An Li |
| collection | DOAJ |
| description | Gas separation membranes have been a hot topic of research in recent decades due to their low costs, high energy efficiency and wide range of applications. Machine learning provide a fast way to design gas separation membranes with required performance. This review systematically describes the process of machine learning-assisted gas separation membrane development. In addition, the experimental data on CO2/CH4, CO2/N2 and O2/N2 separation performance were summarized to provide basis for future work on machine learning-assisted design of gas separation membrane for carbon dioxide capture, and natural gas purification as well as oxygen or nitrogen enrichment. Moreover, we discuss the classical materials that make up gas separation membranes, including MOFs, polymers and COFs, and analyze the strengths and weaknesses of the different materials. Finally, we discuss the challenges in the development of machine learning method for next-generation gas separation membranes. |
| format | Article |
| id | doaj-art-1e21b1a381b1402aa4a6a2b2755188f5 |
| institution | Kabale University |
| issn | 2772-6568 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Carbon Capture Science & Technology |
| spelling | doaj-art-1e21b1a381b1402aa4a6a2b2755188f52025-01-31T05:12:46ZengElsevierCarbon Capture Science & Technology2772-65682025-03-0114100374Machine learning-assisted development of gas separation membranes: A reviewAn Li0Jianchun Chu1Shaoxuan Huang2Yongqi Liu3Maogang He4Xiangyang Liu5Key Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaKey Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaCorresponding author.; Key Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaGas separation membranes have been a hot topic of research in recent decades due to their low costs, high energy efficiency and wide range of applications. Machine learning provide a fast way to design gas separation membranes with required performance. This review systematically describes the process of machine learning-assisted gas separation membrane development. In addition, the experimental data on CO2/CH4, CO2/N2 and O2/N2 separation performance were summarized to provide basis for future work on machine learning-assisted design of gas separation membrane for carbon dioxide capture, and natural gas purification as well as oxygen or nitrogen enrichment. Moreover, we discuss the classical materials that make up gas separation membranes, including MOFs, polymers and COFs, and analyze the strengths and weaknesses of the different materials. Finally, we discuss the challenges in the development of machine learning method for next-generation gas separation membranes.http://www.sciencedirect.com/science/article/pii/S2772656825000144MembraneGas separationMachine learningMetal organic framework |
| spellingShingle | An Li Jianchun Chu Shaoxuan Huang Yongqi Liu Maogang He Xiangyang Liu Machine learning-assisted development of gas separation membranes: A review Carbon Capture Science & Technology Membrane Gas separation Machine learning Metal organic framework |
| title | Machine learning-assisted development of gas separation membranes: A review |
| title_full | Machine learning-assisted development of gas separation membranes: A review |
| title_fullStr | Machine learning-assisted development of gas separation membranes: A review |
| title_full_unstemmed | Machine learning-assisted development of gas separation membranes: A review |
| title_short | Machine learning-assisted development of gas separation membranes: A review |
| title_sort | machine learning assisted development of gas separation membranes a review |
| topic | Membrane Gas separation Machine learning Metal organic framework |
| url | http://www.sciencedirect.com/science/article/pii/S2772656825000144 |
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