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Related Experiment Video

Updated: Jan 20, 2026

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

Yuzi Han1, Wutong Du2, Yonglin Zhang1

  • 1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a data-driven approach using interpretable machine learning to program interfacial polymerization for microencapsulation. This enables quantitative design rules for controlled encapsulation efficiency and particle characteristics.

Keywords:
interfacial polymerizationinterpretable machine learningmicroencapsulationquantitative chemical‐process‐structure‐performancerational design descriptor

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Area of Science:

  • Polymer Science
  • Materials Science
  • Chemical Engineering

Background:

  • Interfacial polymerization (IP) is a versatile technology for membrane design.
  • Microencapsulation (MIP) applications are limited by empirical methods and lack of rational design principles.
  • Conventional membrane theories are insufficient for MIP, which prioritizes encapsulation efficiency (EE%).

Purpose of the Study:

  • To develop quantitative design rules for interfacial polymerization in microencapsulation.
  • To overcome limitations of empirical methodologies in MIP.
  • To establish a data-driven platform for programmable control over microcapsule performance.

Main Methods:

  • Employed interpretable machine learning to program interfacial polymerization.
  • Integrated molecular thermodynamics, polymerization kinetics, and emulsion-stabilized interfacial parameters.
  • Identified key descriptors governing microcapsule formation through a data-driven platform.

Main Results:

  • Established a predictive chemical-process-structure-performance relationship for microencapsulation.
  • Achieved programmable control over encapsulation efficiency (30%-95%).
  • Controlled particle size (100-400 µm) and shell thickness-to-radius ratios (0.005-1) for diverse payloads.

Conclusions:

  • Resolved challenges in understanding multiphase interactions in MIP.
  • Established a new paradigm for quantitative design of polymeric microcapsules.
  • Demonstrated broad implications for functional particles, catalytic microreactors, digital cells, and membranes.