Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches
Ersin Aytaç, Noman Khalid Khanzada, Yazan Ibrahim, Mohamed Khayet, Nidal Hilal, Maxime Pontié · 2024
Read Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches by Ersin Aytaç, Noman Khalid Khanzada, Yazan Ibrahim, Mohamed Khayet, Nidal Hilal, Maxime Pontié (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11677704.txt.
Description
Ersin Aytac leads a team that maps reverse osmosis membrane research. Their study from twenty twenty-four uses many lens types at once. Bibliometric, machine learning, and text mining tools are joined.
More than a thousand articles from a large data set were checked. The field has grown fast, with a surge in recent decades. Thin film composite material is the top focus of experts. Nano materials and polymers also draw much work in the field. Fouling and scaling are the most worked problems of all. Top authors and the top journal are named.
Catalog record
| CREATOR | Ersin Aytaç, Noman Khalid Khanzada, Yazan Ibrahim, Mohamed Khayet, Nidal Hilal, Maxime Pontié |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 79 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |