Reverse osmosis membrane engineering: multidirectional analysis using bibliometric, machine learning, data, and text mining approaches

dc.contributor.authorAytaç, Ersin
dc.contributor.authorKhanzada, Noman Khalid
dc.contributor.authorIbrahim, Yazan
dc.contributor.authorKhayet Souhaimi, Mohamed
dc.contributor.authorHilal, Nidal
dc.date.accessioned2026-01-08T19:48:31Z
dc.date.available2026-01-08T19:48:31Z
dc.date.issued2024-12-06
dc.description© 2024 by the authors.
dc.description.abstractMembrane 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. Journal of Membrane Science 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.
dc.description.departmentDepto. de Estructura de la Materia, Física Térmica y Electrónica
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipMinisterio de Ciencia e Innvación (España)
dc.description.sponsorshipAgencia Estatal de Investigación (España)
dc.description.sponsorshipNew York University Abu Dhabi
dc.description.statuspub
dc.identifier.citationAytaç, E.; Khanzada, N.K.; Ibrahim, Y.; Khayet, M.; Hilal, N. Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches. Membranes 2024, 14, 259, doi:10.3390/membranes14120259.
dc.identifier.doi10.3390/membranes14120259
dc.identifier.essn2077-0375
dc.identifier.officialurlhttps://doi.org/10.3390/membranes14120259
dc.identifier.relatedurlhttps://www.mdpi.com/2077-0375/14/12/259
dc.identifier.urihttps://hdl.handle.net/20.500.14352/129692
dc.issue.number12
dc.journal.titleMembranes
dc.language.isoeng
dc.page.final259-35
dc.page.initial259-1
dc.publisherMDPI
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138389OB-C31/ES/PROCESO SOSTENIBLE DE DESTILACION EN MEMBRANA FOTO-TERMICA PARA LA REUTILIZACION DE AGUA Y RECOLECCION DE ENERGIA AZUL POR ELECTRODIALISIS INVERSA ACERCANDOSE AL RESIDUO CERO/
dc.relation.projectIDCG007
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu53
dc.subject.cdu66.049
dc.subject.keywordReverse osmosis
dc.subject.keywordBiblioshiny
dc.subject.keywordGoogle Gemini
dc.subject.keywordLesch reading ease score
dc.subject.keywordLarge language models
dc.subject.keywordReading time score
dc.subject.keywordTechnical term density
dc.subject.keywordEmotion analysis
dc.subject.ucmFísica (Física)
dc.subject.unesco2210.19 Fenómenos de Membrana
dc.titleReverse osmosis membrane engineering: multidirectional analysis using bibliometric, machine learning, data, and text mining approaches
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number14
dspace.entity.typePublication
relation.isAuthorOfPublication8e32e718-0959-4e6c-9e04-891d3d43d640
relation.isAuthorOfPublication.latestForDiscovery8e32e718-0959-4e6c-9e04-891d3d43d640

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