A COSMO-RS based guide to analyze/quantify the polarity of ionic liquids and their mixtures with organic cosolvents.
A COSMO-RS descriptor (S(sigma-profile)) has been used in quantitative structure-property relationship (QSPR) studies by a neural network (NN) for the prediction of empirical solvent polarity E(T)(N) scale of neat ionic liquids (ILs) and their mixtures with organic solvents. S(sigma-profile) is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge of chemical structures on the polarity (sigma) scale. Firstly, a radial basis neural network exact fit (RBNN) is successfully optimized for the prediction of E(T)(N), the solvatochromic parameter of a wide variety of neat organic solvents and ILs, including imidazolium, pyridinium, ammonium, phosphonium and pyrrolidinium families, solely using the S(sigma-profile) of individual molecules and ions. Subsequently, a quantitative structure-activity map (QSAM), a new concept recently developed, is proposed as a valuable tool for the molecular understanding of IL polarity, by relating the E(T)(N) polarity parameter to the electronic structure of cations and anions given by quantum-chemical COSMO-RS calculations. Finally, based on the additive character of the S(sigma-profile) descriptor, we propose to simulate the mixture of IL-organic solvents by the estimation of the S(sigma-profile)(Mixture) descriptor, defined as the weighted mean of the S(sigma-profile) values of the components. Then, the E(T)(N) parameters for binary solvent mixtures, including ILs, are accurately predicted using the S(sigma-profile)(Mixture) values from the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the E(T)(N) polarity of a wide variety of pure ILs as well as their mixtures with organic solvents, which exhibit significant positive and negative deviations from ideality.
Departamento de Ingeniería Química, Universidad Complutense de Madrid, 28040 Madrid, Spain. pepe.palomar@uam.es
José
J
Palomar
José S
JS
Torrecilla
Jesús
J
Lemus
Víctor R
VR
Ferro
Francisco
F
Rodríguez