Nakhaei-Kohani, Reza;Ali Madani, Seyed;Mousavi, Seyed-Pezhman;Atashrouz, Saeid;Abedi, Ali;Hemmati-Sarapardeh, Abdolhossein;Mohaddespour, Ahmad published 《Machine learning assisted Structure-based models for predicting electrical conductivity of ionic liquids》 in 2022. The article was appeared in 《Journal of Molecular Liquids》. They have made some progress in their research.Recommanded Product: 1-Ethyl-3-methyl-1H-imidazol-3-ium chloride The article mentions the following:
Ionic liquids (ILs) have attracted a great deal of attention as vital compounds that are widely used in various industries, such as the chem. and petroleum industries. Therefore, determining the properties of ionic liquids, such as elec. conductivity (EC), is an important issue. In this study, four different machine learning approaches including K-nearest neighbors (KNN), extreme gradient boosting (XGB), adaptive boosting-decision tree (Ada-DT), and adaptive boosting-support vector regression (Ada-SVR) were established to predict EC of ILs based on two categories namely: (I) smart models based on ILs chem. structure and temperature, and (II) smart models based on ILs thermodn. properties and temperature For this purpose, 2625 laboratory data points corresponding to 225 ILs have been used. The results showed that based on Model (I), KNN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.1976 and 0.9935, resp., is the best model in predicting EC. Also, based on Model (II), the XGB model with RMSE and R2 values of 0.1447 and 0.9965, resp., is the most powerful and accurate model for estimating elec. conductivity Sensitivity anal. revealed that temperature and mol. weight have a direct and inverse effect on increasing the elec. conductivity of ILs, resp. The investigation of the effects of different chem. bonds (in the chem. structure of ionic liquids) on ILs EC showed that -CH2- and > N- substructures have the most neg. and pos. effect on EC, resp. Also, the proposed smart paradigms were capable of accurately predicting the effect cation alkyl chain length on elec. conductivity Based on the leverage technique assessment more than 95% of total data points are in a valid region which indicates that intelligent models are reliable. Consequently, the introduction of intelligent models based on the chem. structure of ILs, the much broader data bank, and the higher accuracy of the proposed models are among the innovations and advantages of this study compared to the previous researches. Findings of this study highlights that chem. structure-intelligence based approaches provide robust, reliable and accurate way to predict EC of ILs. To complete the study, the researchers used 1-Ethyl-3-methyl-1H-imidazol-3-ium chloride (cas: 65039-09-0) .
1-Ethyl-3-methyl-1H-imidazol-3-ium chloride(cas: 65039-09-0) is an imidazolium chloride ionic liquid that can be used as:a solvent as well as catalyst for the depolymerization of oak wood lignin; a solvent in the hydrolysis of hemicellulose (xylan) to xylose using Brønsted acid catalysts.Recommanded Product: 1-Ethyl-3-methyl-1H-imidazol-3-ium chloride
Reference:
Imidazole – Wikipedia,
Imidazole | C3H4N2 – PubChem