Programming Interfacial Polymerization: Machine Learning Unveils Quantitative Rational Design Rules for Microcapsules and Beyond.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Jan 18, 2026

Abstract

Interfacial polymerization (IP) serves as a versatile platform technology for designing polymeric membranes, yet its extension to applications such as microencapsulation (MIP) remains hindered by empirical methodologies, largely due to the absence of quantitative rational design principles. Unlike separation membranes, which prioritize nanostructural control, MIP emphasizes encapsulation efficiency (EE%), rendering conventional membrane-derived theories and thermodynamic descriptors insufficient. In this work, we transcend these limitations by employing interpretable machine learning to program interfacial polymerization, thereby deciphering mechanism-informed quantitative design rules. Our data-driven platform integrates molecular thermodynamics, polymerization kinetics, and emulsion-stabilized interfacial parameters to identify previously overlooked descriptors governing microcapsule formation. We establish a predictive chemical-process-structure-performance relationship and demonstrate programmable control over key performances, including EE% (30%-95%), particle size (100-400 µm), and shell thickness-to-radius ratios (0.005-1) for diverse payloads spanning hydrophobic, hydrophilic, and highly reactive compounds such as toluene diisocyanate and amines. This work not only resolves long-standing challenges in understanding complex multiphase interactions in MIP but also establishes a new paradigm for the quantitative design of polymeric microcapsules, with broad implications for functional particles, catalytic microreactors, digital cells, and membranes.

Authors

  • Yuzi Han
    Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, P. R. China.
  • Wutong Du
    Department of Chemistry and the Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, P. R. China.
  • Yonglin Zhang
    State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.
  • Cheng Qiu
    Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
  • ManKwan Law
    Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, P. R. China.
  • Ying Zhao
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ben Zhong Tang
    Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Centre for Tissue Restoration and Reconstruction, HKUST Jockey Club Institute for Advanced Study, Institute of Molecular Functional Materials, Division of Biomedical Engineering, State Key Laboratory of Molecular Neuroscience, Division of Life Science, Hong Kong University of Science and Technology , Kowloon, Clear Water Bay, Hong Kong.
  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Jinglei Yang
    Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, P. R. China.

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