查看文章

Due to their unique features, deep eutectic solvents (DESs) are well-known as promising and environmentally friendly solvents. Their use in various processes has recently become the focus of several research groups. However, designing DESs with optimal properties for a particular application requires many resources and is time-consuming. Therefore, it is crucial to develop predictive models to estimate the properties of DESs, which will save resources and time. Electrical conductivity is one of the most critical factors for the design, control and optimization of electrochemical processes. In this work, a model capable of estimating the electrical conductivity of DESs is presented. The model combines the Quantitative Structure-Property Relationships (QSPR) approach with artificial neural networks (ANNs) and COSMO-RS-based molecular parameters known as S σ profiles.. The QSPR-ANN training set consists of 2 …