Ionic Liquid Melting Points: Structure–Property Analysis and New Hybrid Group Contribution Model

Abstract

Melting point (T m) 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 T m 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 T m, the complex relationship between the IL structure and T m has resulted in only limited success. In this study, an extensive database of IL T m 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.

The Curious Case of 1-Ethylpyridinium Triflate: Ionic Liquid Exhibiting the Mpemba Effect

“…Among the group contribution-based values provided in Refs. [33,34] for the melting temperatures, T GCM1 = 48.47 °C, T GCM2 = 65.59 °C, T GCM1−R = 39.98 °C, T hybridGCM = 33.51 °C only two last versions of the GC approaches give the values quite close to the melting temperatures obtained by the during the annealing procedure (the largest of two versions obtained in different runs, see Table 1), respectively.…”

Imidazolium-Functionalized Anthraquinone for High-Capacity Electrochemical CO2 Capture

“…The synthesized ionic anthraquinone, [BAQMIM]­[TFSA], exhibited a much lower melting point ( T m = 115 °C; Figure S3) compared to that of AQ ( T m = 286 °C) . Such a significant decrease in T m is attributed to the introduction of an ionic liquid-like moiety into AQ; bulky, asymmetric, and flexible ionic substituents can hinder molecular packing in a crystal lattice, as in the case of typical ionic liquids . The solubility of AQ in nonaqueous solvents is very low, and no significant difference was observed in its solubility in the two organic solvents G4 and DMSO (0.018 M in G4 and 0.011 M in DMSO).…”

“…32 Such a significant decrease in T m is attributed to the introduction of an ionic liquid-like moiety into AQ; bulky, asymmetric, and flexible ionic substituents can hinder molecular packing in a crystal lattice, as in the case of typical ionic liquids. 33 The solubility of AQ in nonaqueous solvents is very low, and no significant difference was observed in its solubility in the two organic solvents G4 and DMSO (0.018 M in G4 and 0.011 M in DMSO). In contrast, [BAQMIM][TFSA] exhibited a significantly enhanced solubility in nonaqueous solvents (0.51 M in G4 and 3.98 M in DMSO; Figure 1a), owing to the presence of ionic liquid-like substituents.…”

Prediction of Liquid Phase Heat Capacity of Ionic Liquids: Comparison of Existing Methods and Development of New Hybrid Group Contribution Models

“…GCMs typically assume that the contributions of functional groups toward the physical properties are additive, which has been shown to perform well for the estimation of certain physical properties such as density and heat capacity . Other properties, such as melting point, cannot be satisfactorily described by additivity, and these models typically require the introduction of parameters that account for the property’s dependency on interactions between functional groups. , …”

“…Furthermore, the physical meaning of GCM parameters can be more intuitive in comparison with some of the complex descriptors of QSPR models. GCMs are well suited for predicting IL physical properties due to the wide variety of possible ILs and their tunable nature . Previously developed GCMs for IL property prediction involved the development of a limited range of contribution parameters because of the small number of synthesized and characterized ILs available at the time.…”