Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches
Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Membranes |
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| Online Access: | https://www.mdpi.com/2077-0375/14/12/259 |
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| author | Ersin Aytaç Noman Khalid Khanzada Yazan Ibrahim Mohamed Khayet Nidal Hilal |
| author_facet | Ersin Aytaç Noman Khalid Khanzada Yazan Ibrahim Mohamed Khayet Nidal Hilal |
| author_sort | Ersin Aytaç |
| collection | DOAJ |
| description | Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009. Thin-film composite (TFC) polymeric material has been the primary focus of RO membrane experts, with 550 articles published on this topic. The use of nanomaterials and polymers in membrane engineering is also high, with 821 articles. Common problems such as fouling, biofouling, and scaling have been the center of work dedication, with 324 articles published on these issues. Wang J. is the leader in the number of published articles (73), while Gao C. is the leader in other metrics. <i>Journal of Membrane Science</i> is the most preferred source for the publication of RO membrane engineering and related technologies. Author social networks analysis shows that there are five core clusters, and the dominant cluster have 4 researchers. The analysis of sentiment, subjectivity, and emotion indicates that abstracts are positively perceived, objectively written, and emotionally neutral. |
| format | Article |
| id | doaj-art-91c515813cfa42eeaca1aee1fe50db40 |
| institution | DOAJ |
| issn | 2077-0375 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Membranes |
| spelling | doaj-art-91c515813cfa42eeaca1aee1fe50db402025-08-20T02:57:02ZengMDPI AGMembranes2077-03752024-12-01141225910.3390/membranes14120259Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining ApproachesErsin Aytaç0Noman Khalid Khanzada1Yazan Ibrahim2Mohamed Khayet3Nidal Hilal4Department of Structure of Matter, Thermal Physics and Electronics, Faculty of Physics, University Complutense of Madrid, Avda. Complutense s/n, 28040 Madrid, SpainNYUAD Water Research Center, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi 129188, United Arab EmiratesNYUAD Water Research Center, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi 129188, United Arab EmiratesDepartment of Structure of Matter, Thermal Physics and Electronics, Faculty of Physics, University Complutense of Madrid, Avda. Complutense s/n, 28040 Madrid, SpainNYUAD Water Research Center, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi 129188, United Arab EmiratesMembrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009. Thin-film composite (TFC) polymeric material has been the primary focus of RO membrane experts, with 550 articles published on this topic. The use of nanomaterials and polymers in membrane engineering is also high, with 821 articles. Common problems such as fouling, biofouling, and scaling have been the center of work dedication, with 324 articles published on these issues. Wang J. is the leader in the number of published articles (73), while Gao C. is the leader in other metrics. <i>Journal of Membrane Science</i> is the most preferred source for the publication of RO membrane engineering and related technologies. Author social networks analysis shows that there are five core clusters, and the dominant cluster have 4 researchers. The analysis of sentiment, subjectivity, and emotion indicates that abstracts are positively perceived, objectively written, and emotionally neutral.https://www.mdpi.com/2077-0375/14/12/259reverse osmosisBiblioshinyGoogle GeminiFlesch reading ease scorelarge language modelsreading time score |
| spellingShingle | Ersin Aytaç Noman Khalid Khanzada Yazan Ibrahim Mohamed Khayet Nidal Hilal Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches Membranes reverse osmosis Biblioshiny Google Gemini Flesch reading ease score large language models reading time score |
| title | Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches |
| title_full | Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches |
| title_fullStr | Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches |
| title_full_unstemmed | Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches |
| title_short | Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches |
| title_sort | reverse osmosis membrane engineering multidirectional analysis using bibliometric machine learning data and text mining approaches |
| topic | reverse osmosis Biblioshiny Google Gemini Flesch reading ease score large language models reading time score |
| url | https://www.mdpi.com/2077-0375/14/12/259 |
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