Researchers at The Hong Kong University of Science and Technology (HKUST) have achieved two major breakthroughs in interfacial polymerization, a key technique for preparing advanced functional materials. By integrating quantum mechanics with machine learning, the team has elucidated the mechanism by which water molecules facilitate reactions at the molecular level. At the same time, they have transformed microcapsule design from a traditional trial-and-error approach into a predictive science.
Together, these two studies demonstrate how fundamental mechanistic insights and data-driven approaches complement one another, highlighting their synergistic roles in advancing interfacial polymerization research. The work was conducted through collaboration among the research team led by Prof. YANG Jinglei, Professor of the Department of Mechanical and Aerospace Engineering at HKUST , the California Institute of Technology, the Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen.
The findings were published in ACS Catalysis , in the paper titled “ Interfacial Polymerization of TEPA and HMDI: The Role of Water” , and in Advanced Materials in “ Programming Interfacial Polymerization: Machine Learning Unveils Quantitative Rational Design Rules for Microcapsules and Beyond ”. The co-first authors of the ACS Catalysis paper are Dr. LIU Biyuan and Dr. ZHANG Yonglin , both postdoctoral fellows in Prof. Yang’s group, while the Advanced Materials study is led by HAN Yuzi, a PhD candidate in the same group.
In one study, the team investigated why the reaction between an amine and an isocyanate proceeds so rapidly at the water-oil interface. Through quantum mechanical calculations, they discovered that a single water molecule can serve as a proton-transfer bridge, significantly lowering the reaction’s energy barrier. This atomistic insight highlights the catalytic role of water and provides a theoretical foundation for controlling reaction kinetics and polymer morphology.
Prof. Yang said, "This work provides us direct evidence of how water facilitates interfacial polymerization at the molecular level. Understanding this mechanism is key to rationally controlling reaction kinetics and the resulting membrane nanomorphology."
In the other study, the team addressed the long-standing reliance on empirical formulations in interfacial polymerization-based microencapsulation. They constructed a comprehensive experimental database and integrated it with interpretable symbolic machine learning algorithms to establish, for the first time, a quantitative design framework that deciphers the complex causal relationships among chemical properties, processing conditions, structure, and performance. This approach further elucidates the rational design rules governing encapsulation efficiency, particle size, and shell thickness, enabling the programmable design of microcapsules with tailored properties and functions.
Prof. Yang noted, "We've transformed microencapsulation from an experience-driven craft into a predictive science. Our AI-driven platform enables the rational design of microcapsules with tailored properties for a wide range of applications, from self-healing materials to drug delivery, by precisely controlling the underlying design principles."
ACS Catalysis
Experimental study
Not applicable
Interfacial Polymerization of TEPA and HMDI: The Role of Water
16-Mar-2026