Extraction of dibenzothiophene from dodecane using ionic liquids as the extracting phase has been investigated for a range of ionic liquids with varying cation classes (imidazolium, pyridinium, and pyrrolidinium) and a range of anion types using liquid-liquid partition studies and QSPR (quantitative structure-activity relationship) analysis. The partition ratio of dibenzothiophene to the ionic liquids showed a clear variation with cation class (dimethylpyridinium . methylpyridinium . pyridinium # imidazolium # pyrrolidinium), with much less significant variation with anion type. Polyaromatic quinolinium-based ionic liquids showed even greater extraction potential, but were compromised by higher melting points. For example, 1-butyl-6-methylquinolinium bis{(trifluoromethyl)sulfonyl}amide (mp 47 uC) extracted 90% of the available dibenzothiophene from dodecane at 60 uC.

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 predicting the melting points of two sets, comprised of 22 and 62 imidazolium-based ionic liquids, respectively. Good correlations and predictions are obtained in both cases. Within the data set selected (only monopositive and mononegative ions are studied, and so total charge was not a factor), the degree of sphericity is the most important variable for the anion, while for the cation the main descriptors pertained to three radial distribution functions that describe three different sections in the cation. These characterise the ionic interactions, the symmetry-breaking region, and the length of the side chains.

We present an optimised artificial neural network (ANN) model for predicting the melting point of a group of 97 imidazolium salts with varied anions. Each cation and anion in the model is described using molecular descriptors. Our model has a mean prediction error of 1.30%, a regression coefficient of 0.99 and a mean P-value of 0.92. The ANN's prediction performance depends mainly on the anion size. In particular, the prediction error decreases as the anion size increases. The high statistical relevance makes this model a useful tool for predicting the melting points of imidazolium-based ionic liquids.