Prediction of the Hildebrand parameter of various solvents using linear and nonlinear approaches

Mohammad Goodarzi, Pablo R. Duchowicz, Matheus P. Freitas, Francisco M. Fernández

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

The Hildebrand solubility parameter (δ) provides a numerical estimate of the degree of interaction between materials, and can be a good indication of solubility. In this work, a small number of physicochemical variables were appropriately selected from a pool of Dragon descriptors and correlated with the Hildebrand thermodynamic parameter of compounds previously studied as organic solvents of buckminsterfullerene (C60), using multiple linear regression and support vector machines. Models were validated using an external set of compounds and the statistical parameters obtained revealed the high prediction performance of all models, especially the one based on nonlinear regression. These findings provide useful information about which solvent and corresponding characteristics are important for solubility studies of e.g. this increasingly useful carbon allotrope.

Original languageEnglish (US)
Pages (from-to)130-136
Number of pages7
JournalFluid Phase Equilibria
Volume293
Issue number2
DOIs
StatePublished - Jun 25 2010

Keywords

  • Artificial neural networks
  • Fullerene
  • Hildebrand parameter
  • QSPR

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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