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A smart framework to design membranes for organic micropollutants removal

Nature Sustainability volume 8, pages 1177–1189 (2025)Cite this article

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Abstract

Developing polymeric membranes that effectively remove organic micropollutants (OMPs) is important for water management. However, the structural diversity and physiochemical variability of OMPs make it challenging to develop such membranes. Here we present a data-mechanism-integrated approach to assist membrane design. This approach integrates molecular fingerprint and physical models within the machine learning framework to quantify how functional groups in OMPs affect removal by polymeric membranes and to elucidate the removal mechanisms. We uncovered an anomalous multigroup coupling effect in membrane-based OMP removal and showed that the efficiency of removal depends on the influence of the functional group coupling in the molecular structure. This finding challenges the conventional approach in membrane screening and design that focuses on the properties of isolated functional groups. By combining this knowledge with assessments of OMP types and membrane properties, we reveal a comprehensive interaction framework for tailoring OMP-removal membranes. Overall, the data-mechanism co-driven paradigm has the potential to facilitate the development of advanced water-treatment membranes, eventually contributing to sustainable water management and the preservation of a safe water environment.

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Fig. 1: Construction of DMF-MRL.
Fig. 2: Quantification of the importance of OMP primary substructures by DMF-MRL.
Fig. 3: Quantification of the importance of OMPs’ secondary substructures by DMF-MRL.
Fig. 4: Construction of interaction knowledge framework.
Fig. 5: The design of OMP-removal membranes.

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Data availability

The data supporting the findings of this study are available in the paper and its Supplementary Information files.

Code availability

The model files used in this work are accessible on GitHub (https://github.com/effortV/A-smart-framework-to-design-membranes-for-organic-micropollutants-removal.git).

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Acknowledgements

We thank the National Key R&D Program of China (2023YFB3810900), the National Natural Science Foundation of China (22138010 and 52261145697), the ‘Pioneer’ and ‘Leading Goose’ R&D Program of Zhejiang (2024C03132), the Fundamental Research Funds for the Central Universities (226-2024-00091), Ecological Civilization Project of Zhejiang University, the China Postdoctoral Science Foundation (2023M742997) and the CRSRI Open Research Program (CKWV20231180/KY) for financial support.

Author information

Author notes

  1. These authors contributed equally: Dan Lu, Zihang Zhao, Xinchen Xiang.

Authors and Affiliations

  1. Engineering Research Center of Membrane and Water Treatment of MOE, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, P. R. China

    Dan Lu, Xinchen Xiang, Yifang Geng, Yangyang Li, Shiying Xu, Chuanqi Zhang, Zhikan Yao & Lin Zhang

  2. Future Environment Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, P. R. China

    Dan Lu, Zhikan Yao & Lin Zhang

  3. College of Automation, Hangzhou Dianzi University, Hangzhou, P. R. China

    Zihang Zhao, Tianyu Li, Ming Wu, Lijun Liang & Kai Fan

  4. Changjiang River Scientific Research Institute, Wuhan, P. R. China

    Zhuofan Gao

  5. School of Pharmacy, Hangzhou Normal University, Hangzhou, P. R. China

    Jia-Wei Shen

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Contributions

D.L., L.L. and L.Z. conceived of the idea and designed the research. Z.Z. and T.L. performed the DMF-MRL deployment. D.L. and X.X. performed the membrane fabrications and characterizations. D.L. and M.W. performed the MD simulations. D.L., X.X., Y.G., Y.L., S.X. and C.Z. constructed the combinatorial database. Z.G., J.-W.S. and Z.Y. provided constructive suggestions for the results and discussion. D.L., L.L. and L.Z. contributed to the writing of the paper. All authors discussed the results.

Corresponding authors

Correspondence to Lijun Liang or Lin Zhang.

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The authors declare no competing interests.

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Nature Sustainability thanks Yongsheng Chen, Tiezheng Tong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–37, Tables 1–9, Discussion and References.

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Lu, D., Zhao, Z., Xiang, X. et al. A smart framework to design membranes for organic micropollutants removal. Nat Sustain 8, 1177–1189 (2025). https://doi.org/10.1038/s41893-025-01617-6

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