Heat capacity prediction of complex molecules by mass connectivity index

Laboratoire d’Ingénierie des Procédés d’Environnement (LIPE), Département de Chimie Industrielle, Universite Mentouri, Constantine, Algeria

Abstract

Heat capacity prediction and estimation methods of solid organic compounds in terms of temperature are limited, particularly concerning complex molecules with functional groups such as active principles and intermediaries used in pharmaceutical field.

Recently a correlation between heat capacity at constant pressure (Cp), temperature and a new concept named mass connectivity index (MCI), for ionic liquids, was published [1-3]. In this predictive method, heat capacity can be calculated at different temperatures, if standard heat capacity at 298.15 K is known. The effect of molecular structure on heat capacity is accounted for in this model by the mass connectivity index, a molecular descriptor, which differentiates between compounds. The Valderrama generalized correlation admits, in addition, two universal coefficients, which are obtained from experimental data regression.

In the present work, a similar approach is used to predict solid state heat capacity of organics and pharmaceutical products. In order to find model parameters, a database was grouped comprising (104) different compounds and a set of more than 5,791 experimental values of solid state Cps obtained from literature. These collected data were used in multiple linear regression to find model parameters. It was found that the values of predicted heat capacities of compounds non-included in the database were good; they are quite close to the ones presented in the literature. Moreover, this method is simple to use, since only molecular structure of the component and its solid state heat capacity at 298.15 K should be known.