Search

An approach to predict the stability constants of coronates from the properties of solvents, cations, and crown ethers has been developed based on exploratory and neural network methods for mathematical modeling of equilibria in solutions. Exploratory (factor, cluster, discriminant, canonical, decision trees), regression, and neural network (supervised and Kohonen network) models of the stability of crown ethers (12С4, 13С4, 14С4, 15С4, 15С5, 18С6, 21С7, 24С8, B12C4, B15C5, CH15C5, CH18C6, DCH18C6, DCH21C7, DB18C6, DB21C7, DB24C8, DB27C9, and DB30C10) complexes with cations of alkali (Li+, Na+, K+, Cs+, Rb+) and alkaline-earth (Ca2+, Sr2+, Ba2+) metals in aqueous and non-aqueous (acetone, acetonitrile, dimethyl sulfoxide, methanol, pyridine, dimethylformamide, dioxane, propylene carbonate, 1,2-dichloroethane, and nitrobenzene) solutions have been developed according to the properties of solvents (diameter of solvent molecule, Kamlet–Taft parameter, Dimroth–Reichardt parameter, dielectric constant), crown ethers (Balaban topological index), and cations (cation diameter) at 298.15 K. [ABSTRACT FROM AUTHOR]

Models of multiple linear regression and multilayer artificial neural network have been developed for modeling and predicting the stability constants of sodium and potassium coronates basing on the properties of aqueous-organic solvents (water-methanol, water-propan-2-ol, water-acetonitrile, and water-acetone). The values of the coronates stability constants in water-ethanol solvents have been predicted, and the predictions of the models of multiple linear regression and an artificial neural network models have been compared. The contributions of electrostatic, cohesive, and electron-donating interactions to the increase in the stability of the coronates have been quantitatively assessed basing on the models of multiple linear regression and the principle of free energies linearity. Neural network models based on unsupervised (multilayer perceptrons) and supervised (Kohonen networks) learning algorithms have been developed to classify the stability of sodium and potassium coronates. The neural network classifiers have fully confirmed the classification of the coronated stability via the k-means exploration method. [ABSTRACT FROM AUTHOR]

Linear Fisher classification functions, canonical linear discriminant functions, and trees (rules) for classification and prediction of the stability of sodium (18-crown-6Na+) and potassium coronates (18-crown-6K+) from the properties of aqueous-organic solvents (water—methanol, water—propan-2-ol, and water—acetonitrile) have been obtained basing on multivariate exploratory analysis. The proposed approach to prediction of the coronates stability has been verified using independent experimental data on the stability constants of sodium and potassium coronates in water—dioxane and water—acetone mixtures. The constructed classification functions and rules have possessed high predictive potential. [ABSTRACT FROM AUTHOR]