No abstract

A multiparameter artificial neural network (ANN) approach was successfully utilized to predict the solubility of C60 in different solvents. Molar volume, polarizability parameter, LUMO energy, saturated surface, and average polarizability molecular properties were chosen to be the most important factors determining the solubilities. The results show that in a large number of solvents (126) the solubility decreases with increasing molar volumes of the solvents and increases with their polarizability and saturated surface areas. A method is suggested to the approximate determination of experimentally not easily measurable solubility related thermodynamic parameters, e.g., the Hildebrand parameter, based on reliable solubility measurements.

No abstract

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.