Using Molecular Simulation to Predict Solvation and Partition Coefficients in Solvents of Different Polarity

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Nuno M. Garrido (Autor) FEUP; Miguel Jorge (Autor) FEUP; António J. Queimada (Autor) FEUP; Eugénia A. Macedo (Autor) FEUP; Ioannis G. Economou (Autor) FEUP

Revista

Título: Physical Chemistry Chemical Physics

Vol. 13 Nº 20

Páginas: 9155-9164

ISSN: 1463-9076

Editora: Royal Society of Chemistry

Indexação

ISI Web of Knowledge

ISI Web of Science

Scopus

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia química

CORDIS: Ciências Tecnológicas

Projetos Associados

LA 20 - Projeto Estratégico-LA 20 - 2011-2012

Outras Informações

DOI: 10.1039/c1cp20110g

Abstract (EN): A methodology is proposed for the prediction of the Gibbs energy of solvation (Delta(Solv)G) based on MD simulations. The methodology is then used to predict DSolvG of four solutes (namely propane, benzene, ethanol and acetone) in several solvents of different polarities (including n-hexane, n-hexadecane, ethylbenzene, 1-octanol, acetone and water) while testing the validity of the TraPPE force field parameters. Excellent agreement with experimental data is obtained, with average deviations of 0.2, 1.1, 0.8 and 1.2 kJ mol(-1), for the four solutes respectively. Subsequently, partition coefficients (log P) for forty different solute/solvent systems are predicted. The a priori knowledge of partition coefficient values is of high importance in chemical and pharmaceutical separation process design or as a measure of the increasingly important environmental fate. Here again, the agreement between experimental data and simulation predictions is excellent, with an absolute average deviation of 0.28 log P units. However, this deviation can be decreased down to 0.14 log P units, just by optimizing partial atomic charges of acetone in the water phase. Consequently, molecular simulation is proven to be a tool with strong physical basis able to predict log P with competitive accuracy when compared to the popular statistical methods with weak physical basis.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10