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Review synthesizes crosslinking and machine learning strategies for polyimide organic solvent nanofiltration membranes, highlighting pathways to durable and sustainable industrial separation.
Organic solvent nanofiltration (OSN) has emerged as an energy‐efficient alternative to conventional separation processes for molecular‐level separations under mild operating conditions. Among the various membrane materials investigated, polyimides (PIs) have attracted significant attention because of their excellent thermal stability, mechanical robustness, and chemical resistance. However, conventional PI membranes often suffer from solvent‐induced swelling and structural instability, limiting their long‐term industrial applicability. To overcome these challenges, extensive research has focused on advanced crosslinking strategies, sustainable fabrication methods, and emerging data‐driven membrane design approaches. Chemical modifications such as diamine crosslinking, metal‐ion coordination, and synergistic multistep crosslinking have significantly enhanced solvent resistance and separation performance, achieving solute rejection values exceeding 99% in harsh solvents including DMF. Simultaneously, sustainable fabrication approaches involving green solvents and bio‐derived materials are gaining increasing importance. In parallel, artificial intelligence (AI) and machine learning techniques are being integrated into membrane research for predictive modeling, optimization, and virtual screening of membrane performance. This review critically discusses recent advances in PI‐based OSN membranes, emphasizing crosslinking chemistry, sustainable fabrication, and AI‐assisted membrane design while highlighting current challenges and future industrial prospects.
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Ravi et al. (2026) studied this question.
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