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. 2024 Dec 6;14(12):259.
doi: 10.3390/membranes14120259.

Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

Ersin Aytaç  1   2 , Noman Khalid Khanzada  3 , Yazan Ibrahim  3   4 , Mohamed Khayet  1   5 , Nidal Hilal  3
Affiliations

Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

Ersin Aytaç et al. Membranes (Basel). .

Abstract

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. 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.

Keywords: Biblioshiny; Flesch reading ease score; Google Gemini; emotion analysis; large language models; reading time score; reverse osmosis; technical term density.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Yearly publications, average global citations per document published in the corresponding year (AGCDCy) and average normalized global citations per document published in the corresponding year (ANGCDCy) results of the collection.
Figure 2
Figure 2
Classification of publications in terms of used polymeric material.
Figure 3
Figure 3
(a) Surface-engineered RO membranes and their work devotion and (b) publication years of corresponding articles.
Figure 4
Figure 4
Important metrics of top 10 scientists in the reverse osmosis membrane engineering domain based on the number of publications.
Figure 5
Figure 5
Co-authorship analysis of the authors (weights = documents, min. number of documents of an author = 25, clusters with single items removed).
Figure 6
Figure 6
Important metrics of top 10 journals publishing on RO membrane engineering based on the number of articles.
Figure 7
Figure 7
Most relevant affiliations.
Figure 8
Figure 8
Metrics of top 10 articles (based on global citations) [48,128,200,211,220,294,295,296,297,298] in the reverse osmosis membrane engineering domain.
Figure 9
Figure 9
Most cited references (top 10) [138,300,301,302,303,304,305,306,307,308,309] by the RO membrane engineering community.
Figure 10
Figure 10
Text mining on abstracts of the articles: (a) Reading time score, (b) Flesch reading ease score, and (c) technical term density ratio (%).
Figure 11
Figure 11
Overlap percentages of author keywords in (a) titles, (b) abstracts, (c) overlap percentage, (d) cosine distance scores between author keywords and extracted keywords by Gemini.
See this image and copyright information in PMC

References

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