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Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Colloid and Surface Science

Background:

  • Mixed surfactant systems offer enhanced properties over single surfactants for practical applications.
  • Solute partitioning in mixed micelles is less understood than in single surfactant systems.
  • Accurate prediction of partition equilibria requires knowledge of mixed micelle composition.

Purpose of the Study:

  • To investigate surfactant compositions within mixed micelles using molecular dynamics (MD) simulations.
  • To compare the predictive capabilities of COSMO-RS and COSMOmic models for partition equilibria in mixed micelles.
  • To evaluate the prediction of ionizable molecule partitioning, considering micelle structure and protolytic equilibria.

Main Methods:

  • Molecular dynamics (MD) self-assembly simulations to determine micelle composition.
  • Application and comparison of COSMO-RS and COSMOmic models for predicting partition coefficients.
  • Thermodynamic cycle calculations using COSMOmic to determine position-dependent pKa for ionizable solutes.

Main Results:

  • MD simulations successfully determined surfactant compositions in mixed micelles.
  • Both COSMO-RS and COSMOmic provided reasonable predictions for neutral molecules.
  • COSMOmic, incorporating micelle's 3D structure, showed better agreement with experimental data.
  • COSMOmic enabled accurate prediction of ionized isovanillin partitioning by accounting for its protolytic equilibrium within micelles.

Conclusions:

  • MD simulations are effective for characterizing mixed micelle composition.
  • COSMOmic is a valuable tool for predicting solute partitioning in mixed surfactant systems, outperforming COSMO-RS for accuracy.
  • Accounting for the solute's ionization state within the micelle is crucial for reliable partition coefficient predictions.