Review of supercooling suppression of phase change materials based on nanoparticles

作     者：Gao, Yuguo Zhao, Yanan Wang, Xinyu Mohit, Mohammaderfan Xu, Minghan Sasmito, Agus P. 

作者机构：North China Univ Water Resources & Elect Power Sch Mech Engn Henan Int Joint Lab Thermo Fluid Electrochem Syst Zhengzhou 450045 Henan Peoples R China McGill Univ Dept Min & Mat Engn 3450 Univ St Montreal PQ H3A 0E8 Canada 

出 版 物：《THERMOCHIMICA ACTA》 (Thermochim Acta)

年 卷 期：2025年第745卷

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0807[工学-动力工程及工程热物理] 0703[理学-化学] 

基　　金：Key Research and Development and Promotion Project in Henan province-Science and Technology (International Cooperation) Introduce Intelligence Project of Henan Province [GH2019021, HNGD2020044, HNGDB2021003] Henan Province of China Natural Sciences and Engineering Research Council of Canada [NSERC RGPIN-2021-02901] Fonds de Recherce du Quebec [FRQ-NT-PR-300597] 

主　　题：Nanoparticles PCM Supercooling Machine learning Numerical modeling Nucleation 

摘      要：The phenomenon of supercooling in phase change materials has been a major obstacle to the effective use of these materials in thermal energy storage systems. Numerous studies have shown that nanoparticles display significant advantages over other methods of supercooling inhibition in terms of increased nucleation rate, enhanced thermal conductivity, reduced supercooling, and improved cycling stability. Yet, the mechanism of supercooling inhibition by nanoparticles has not been comprehensively discussed or reviewed in published articles. The objective of this review is to provide a comprehensive analysis of the mechanisms by which nano- particles promote nucleation and reduce supercooling in phase change materials, as well as to discuss the most influential factors such as the type, concentration, and size of the nanoparticles, as well as ultrasonic and synergistic effects. Additionally, the paper focuses on an overview of recent advances in the application of machine learning to control the supercooling of nanofluid phase change materials. The potential for practical applications of machine learning techniques to enhance the thermophysical properties of phase change materials and suppress phase change material supercooling is one of our major findings.