Abstract:The challenge of predicting the melting point of ionic liquids is outlined. A descriptor modelling approach for two separate sets of ionic liquids is presented. In each case, the cations and the anions are modelled separately, using quantitative structure-property relationships. Both models include constitutional, topological and geometric descriptors as well as quantum mechanical ones. This approach gives access to (nxm) ionic liquids using only (n+m) calculations. The protocol is tested and validated for pre… Show more
“…They should account for the size and shape effects of the molecules in intermolecular interactions. The quantum mechanical descriptors d 17 to d 19 and d 28 reflect the interatomic interactions averaged by the number of the atoms for a given molecule. The predicted values of log L for ILs I to IV are listed in Table 3, and the plot of the predicted versus observed plots for ILs I to IV is shown in Figure 1a to d (the remaining results for ILs V to VIII are given in Supporting Information SM1).…”
Section: Qspr Modeling Of Ostwald Solubility Coefficient (Log L)mentioning
Interactions of solutes with diverse ionic liquid solvents have been investigated by quantitative structureproperty relationship (QSPR) methodology. Ostwald solubility coefficients and partition coefficients of organic solutes in eight different ionic liquids are correlated by molecular descriptors calculated solely from their structures. Two-to four-parameter best multilinear regression models were obtained with coefficients of determination ranging from 0.913 to 0.992. Additionally, several models were obtained with the same descriptors for all eight ionic liquids (ILs). Charge-related type descriptors contributed significantly to most of the models. The QSPR models were validated using the leave-one-out cross validation method.
“…They should account for the size and shape effects of the molecules in intermolecular interactions. The quantum mechanical descriptors d 17 to d 19 and d 28 reflect the interatomic interactions averaged by the number of the atoms for a given molecule. The predicted values of log L for ILs I to IV are listed in Table 3, and the plot of the predicted versus observed plots for ILs I to IV is shown in Figure 1a to d (the remaining results for ILs V to VIII are given in Supporting Information SM1).…”
Section: Qspr Modeling Of Ostwald Solubility Coefficient (Log L)mentioning
Interactions of solutes with diverse ionic liquid solvents have been investigated by quantitative structureproperty relationship (QSPR) methodology. Ostwald solubility coefficients and partition coefficients of organic solutes in eight different ionic liquids are correlated by molecular descriptors calculated solely from their structures. Two-to four-parameter best multilinear regression models were obtained with coefficients of determination ranging from 0.913 to 0.992. Additionally, several models were obtained with the same descriptors for all eight ionic liquids (ILs). Charge-related type descriptors contributed significantly to most of the models. The QSPR models were validated using the leave-one-out cross validation method.
“…Therefore, the prediction of physical, chemical, toxicological and biological features of ionic liquids should be based on quantitative methods able to correlate their structure to the property of interests rather than on an "intuitive similarity" that can be misleading and often wrong. [3] Examples of such a quantitative structure-property relationship approach were recently described and used, for example, to correlate ionic liquid descriptors with their melting points [4] and with the partition ratios of dibenzothiophene in different ionic liquids. [5] Notwithstanding the complexity of the issue, it is accepted that in many instances ionic liquids are a viable green alternative to conventional molecular solvents.…”
“…[36] Again, the influence of the anion was overlooked, for this methodology was applied to a set of ILs based on pyridinium bromide. López-Martin et al [37] reported the first study in which both ions were taken into account to build up a model for predicting the melting point of ILs. Their unique approach consists of optimising the geometries of both ions separately, by means of semi-empirical AM1 calculations; then descriptors are derived with CODESSA for each ion, and finally ion descriptors are paired up for each ionic liquid to develop a model by means of partial least squares analysis.…”
Section: Reports On Modelling Melting Point For Ionic Liquidsmentioning
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