While several group contribution method (GCM) models have been developed in recent years for the prediction of ionic liquid (IL) properties, some challenges exist in their effective application. Firstly, the models have been developed and tested based on different datasets; therefore, direct comparison based on reported statistical measures is not reliable. Secondly, many of the existing models are limited in the range of ILs for which they can be used due to the lack of functional group parameters. In this paper, we examine two of the most diverse GCMs for the estimation of IL melting point; a key property in the selection and design of ILs for materials and energy applications. A comprehensive database consisting of over 1300 data points for 933 unique ILs, has been compiled and used to critically evaluate the two GCMs. One of the GCMs has been refined by introducing new functional groups and reparametrized to give improved performance for melting point estimation over a wider range of ILs. This work will aid in the targeted design of ILs for materials and energy applications.
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.
Thermal conductivities of nine different bis((trifluoromethyl)sulfonyl)imide-based
ionic liquids (ILs), [C2mim][NTf2], [C4mmim][NTf2], [C8mim][NTf2], [P1,4,4,4][NTf2], [N1,1,1,4][NTf2], [N1,8,8,8][NTf2], [C4
3mpy][NTf2], [C4
4mpy][NTf2], and [C4mpy][NTf2], have been measured using
the modified transient plane source method (MTPS) over the temperature
range from 298 to 348 K at atmospheric pressure. The thermal conductivities
of all the studied ILs are in the range of 0.120–0.150 W·m–1·K–1, with a combined expanded
uncertainty of 0.01 W·m–1·K–1 at a 95% confidence level. In all cases, thermal conductivity decreases
with increasing temperature and follows a linear trend, as is typical
for most liquids and falls within the reported range for ILs. Two
existing group contribution models (GCMs) with differing modeling
philosophies have also been extended and tested against a database
consisting of this new data combined with all existing IL thermal
conductivity data in the literature. The extended GCMs yielded average
absolute relative deviations of 5.5% and 4.9%, respectively, for the
entire dataset.
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