A Comparative Study of COSMO-Based and Equation-of-State Approaches for the Prediction of Solvation Energies Based on the COMPSOL Databank

A Comparative Study of COSMO-Based and Equation-of-State Approaches for the Prediction of Solvation Energies Based on the COMPSOL Databank

The computation of solvation energies has many uses in several fields, such as design of separation processes, pharmacology and drug-design, and kinetic modeling. These applications require thermodynamic models capable of accurately predicting solvation energies, accounting for the temperature dependency of this property, and which are fast and robust. Within this framework, we compared two COSMO-based continuum solvation models (COSMO-RS and COSMO-SAC-dsp) with two versions of predictive cubic equations of state well-acknowledged for their efficiency (PSRK and UMR-PRU). For this purpose, a large experimental set of 65,000 datapoints extracted from the COMPSOL databank was considered. Comparisons between computed and experimental data were performed for Gibbs solvation energy and, for the first time in the literature, for both entropy and enthalpy of solvation simultaneously. For simpler binary mixtures, in which hydrogen bonding does not take place, all models were capable of providing accurate predictions, with average absolute deviations below 0.3 kcal/mol regarding the solvation Gibbs energy. For more complex associating mixtures, COSMO-RS showed the best correlation between experimental and calculated data, especially for aqueous systems; among EoS, it is observed that the PSRK model offers the best accuracy.

The estimation of solvation Gibbs energy (Δsolv gᵢ) is an important issue in process and product design. Given that this thermodynamic quantity is related to the degree of affinity between different chemical species, it can be very useful, for example, in the selection of an adequate solvent for a separation process or a chemical reaction. The computation of solvation quantities is also a critical matter in pharmacology and drug-design, since the solvation phenomenon directly affects the affinity of a given biologically active substance for target proteins, as well as its solubility and chemical stability [1–4].

Solvation energies are also required for liquid-phase detailed chemical kinetic models, that involve a large number of reactions and chemical species, and are based on gas-phase models developed to simulate pyrolysis, combustion or atmospheric oxidation phenomena [5–8]. These examples highlight the need for predictive and accurate methods that can predict solvation energies and at the same time are fast and robust and can be applied to a wide range of chemical compounds. As a noticeable feature, these applications cover large ranges of temperature and therefore, models used for solvation energies must include a temperature dependency as a prerequisite.