Machine learning-driven multi-objective optimization of electrospun nanofibrous membranes design for membrane distillation.

Saved in:
Bibliographic Details
Title: Machine learning-driven multi-objective optimization of electrospun nanofibrous membranes design for membrane distillation.
Authors: Li X; School of Energy and Environment, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China., Yan KQ; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China., Wei X; School of Energy and Environment, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China., Li M; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China., Sun J; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China., Bi Y; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China., Shao L; MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, State Key Laboratory of Urban-Rural Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China., Gao H; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China. Electronic address: hanyugao@ust.hk., An AK; Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China. Electronic address: alicia.kjan@ust.hk.
Source: Water research [Water Res] 2026 Aug 15; Vol. 301, pp. 126093. Date of Electronic Publication: 2026 May 09.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2448
DOI:10.1016/j.watres.2026.126093