液相定压比热容是一种关键基础热力学性质，对于下一代替代制冷剂的开发和研究具有重要意义。由于现有实验条件及理论方法的限制，目前对于具有低温室效应潜能的潜在替代制冷剂，尤其是氢氟烯烃及其混合物的液相定压比热容性质仍缺乏足够的认识。.本项目将从氢氟烃类制冷剂（包括氢氟烷烃及氢氟烯烃）所共同具有的剩余定压比热容相似性规律出发，结合量化构效关系分析、分子模拟计算及基于Helmholtz自由能的分子热物性模型对该相似性进行解释，揭示其分子尺度微观机理。在此基础上，利用统计力学对应态原理和神经网络方法构建适用于未知氢氟烃类纯质及混合制冷剂液相定压比热容的预测模型。采用准稳态量热法获取具有代表性的氢氟烃制冷剂实验数据，对预测模型及微观机理分析结果进行外部验证。最后，利用氢氟烃类混合制冷剂超额定压比热容的变化规律研究混合过程的非随机特性，并在热力学框架下，探索不同超额能量性质的统一建模方法。

Liquid isobaric heat capacity is a key fundamental thermodynamic property, which is essentially important in the development and research for next generation alternative refrigerants. Due to the limitation of available experimental and theoretical methods, little knowledge has been understood for the liquid heat capacity of low global warming potential refrigerants, especially the hydrofluoroolefins and their mixtures..Starting from the detected universal corresponding state similarity of reduced residual isobaric heat capacity among hydrofluorocarbon refrigerants (including both hydrofluoroalkanes and hydrofluoroolefins), this project attempts to reveal the microscopic mechanism for this attractive similarity by quantitative structure-property relationship analysis, molecular simulation and molecular models explicit in Helmholtz free energy. Based on the microscopic study, predictive models for the isobaric heat capacity of unknown refrigerants will be established by applying corresponding state principle in statistical mechanics and artificial neural network. Obtained new experimental heat capacity data from quasi-state calorimetric measurements will be used to confirm the reliability of the predictive models. Finally, excess heat capacity for mixed refrigerants will be analyzed to determine the non-random mixing characteristic and to explore the establishment of uniform model for different excess energy properties.