A novel method for the surface tension estimation of ionic liquids based on COSMO-RS theory

• Published in: Fluid phase equilibria Vol. 468; pp. 9 - 17
• Main Authors: Járvás, Gábor, Kontos, János, Babics, Gabriella, Dallos, András
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 25.07.2018
• Subjects: COSMO-RS; Estimation; Ionic liquids; Neural network; Surface tension; Ionic liquids; Surface tension; COSMO-RS; Neural network; Estimation
• ISSN: 0378-3812; 1879-0224
• DOI: 10.1016/j.fluid.2018.04.010

In the presented study, COSMO-RS sigma moments were utilized in a nonlinear multivariate QSPR model for the estimation of the temperature dependent surface tension of various ionic liquids in wide surface tension (15.5–65.1 mN m−1) and temperature (268–533 K) range. The developed model is supposed to be a generally applicable, robust and accurate method for the prediction of ionic liquid's surface tension. 880 data points ensure its reliability, which values were used to establish, validate and test the method. An artificial neural network was developed, optimized and used as regression model. The prediction power of the proposed model was validated with an external data set, with a squared correlation coefficient R2 = 0.97 and a mean absolute error MAE = 1 mN m−1. The estimation ability of the model was compared against widely known methods. Furthermore, the factor sensitivity analyses of the utilized molecular descriptors allowed shedding light on the intermolecular basis of ionic liquid's surface tension. Results showed that the kind of skewness of the sigma profile, the electrostatic interaction energy and the hydrogen bonding acceptor function play the most important roles in the formation of ionic liquid's surface tension.