群(周期表)
离子液体
熔点
GCM转录因子
热力学
表征(材料科学)
计算机科学
财产(哲学)
简单(哲学)
化学
生化工程
材料科学
有机化学
物理
大气环流模式
纳米技术
工程类
哲学
认识论
催化作用
生态学
气候变化
生物
作者
Dhruve Kumar Mital,Paul Nancarrow,Taleb Ibrahim,Nabil Abdel Jabbar,Mustafa Khamis
标识
DOI:10.1021/acs.iecr.1c04292
摘要
Melting point (Tm) is one of the defining characteristics of ionic liquids (ILs) and is often one of the most important factors in their selection for applications in separation processes, lubrication, or thermal energy storage. Due to the almost limitless number of theoretically possible ILs, each with incrementally different physiochemical properties, there is significant scope for designing ILs for specific applications. However, the need for extensive synthesis and experimental characterization to find the optimum IL is a major barrier. Therefore, it is essential that predictive tools are developed for estimating the physiochemical properties of ILs. The starting point for any such approach should be the prediction of Tm since most other property models will be based on the assumption that the IL is in the liquid phase at the application temperature. While several attempts have previously been made at developing group contribution methods (GCMs) for estimating IL Tm, the complex relationship between the IL structure and Tm has resulted in only limited success. In this study, an extensive database of IL Tm has been compiled and used as the basis for a top-down structure–property analysis. Based on the findings, a new hybrid GCM has been developed, which combines functional group parameters with simple, indirect structural parameters derived from the structure–property analysis. The new hybrid GCM has a mean absolute percentage error (MAPE) of 8.6% over the dataset of around 1700 data points and performs quantitatively and qualitatively better than the standard GCM approach.
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