The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

中文翻译：

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离子液体无限稀释选择性的 QSPR 建模

萃取剂和夹带剂的明智选择可以改进当前的混合物分离技术，从而提高工业和实验室实践中使用的化学过程的效率和可持续性。最有前途的方法是直接比较潜在候选者之间无限稀释的选择性。然而，对于大多数化合物而言，在无限稀释值下的选择性很少可用，因此非常需要理论估计。在这项研究中，我们建议采用定量结构-性质关系 (QSPR) 方法来模拟离子液体无限稀释时的选择性。此外，还开发了辅助模型来克服溶质无限稀释时大活度系数的潜在偏差。来自 SelinfDB 数据库的数据被用作 QSPR 模型开发中的训练和内部验证集。使用文献数据进行外部验证。最佳模型的选择是使用决策函数完成的，该函数旨在减少与代表性不足的离子液体或极端温度相关的数据点预测中的偏差。最佳模型用于虚拟筛选苯胺 + 正十二烷混合物的潜在共沸破胶剂。筛选的主题是离子液体组合库，该库基于之前未使用的来自 SelinfDB 的阳离子和阴离子组合以及测试集提取剂创建。无限稀释的选择性和辅助模型在验证中都显示出良好的性能。在适用的情况下，我们的模型的预测与 COSMO-RS 的预测进行了比较，显示较小的预测误差。提出了从正十二烷中提取苯胺的最佳离子液体。