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Wayne State University experimental descriptor database for use with the solvation parameter model.

Colin F Poole1

  • 1Department of Chemistry, Wayne State University, Rm 185 Chemistry, Detroit, MI 48202, United States.

Journal of Chromatography. A
|January 20, 2020
PubMed
Summary

This study compiles experimental data for the solvation parameter model, enhancing its accuracy. The new descriptor database offers a more robust characterization of separation systems for various compounds.

Keywords:
Compound descriptorsGas chromatographyIntermolecular interactionsLiquid-liquid partition constantsReversed-phase liquid chromatographySolvation parameter modelSolver method

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Area of Science:

  • Physical chemistry
  • Quantitative structure-property relationships
  • Separation science

Background:

  • The solvation parameter model uses six descriptors to quantify intermolecular interactions.
  • Currently, only McGowan's characteristic volume (V) is easily calculated; others rely on experimental data.
  • Experimental descriptors include refractive index (E), dipolarity/polarizability (S), hydrogen-bonding (A, B), and partition constants (L).

Purpose of the Study:

  • To assemble a comprehensive descriptor database for the solvation parameter model.
  • To improve the reliability of descriptors used in separation, chemical, biological, and environmental processes.
  • To develop tools for identifying unreliable experimental data within the database.

Main Methods:

  • Compiled gas and reversed-phase liquid chromatography retention factors.
  • Included liquid-liquid partition constants determined in a single laboratory.
  • Developed selection tools to identify potentially unreliable experimental values.

Main Results:

  • Created a descriptor database for a wide range of compounds.
  • Facilitated experimental technique control through single-laboratory measurements.
  • Provided tools for data quality assessment.

Conclusions:

  • The assembled descriptor database is more robust for characterizing separation systems.
  • This approach enhances the quantitative structure-property relationship modeling for diverse applications.
  • Improved descriptor reliability supports accurate prediction of intermolecular interactions.