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Combining random walk and regression models to understand solvation in multi-component solvent systems.

Ella M Gale1, Marcus A Johns2, Remigius H Wirawan3

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A random walk model effectively predicts polysaccharide solubility in ionic liquid-co-solvent mixtures. This model simplifies complex systems, aiding in solvent selection for cellulose processing.

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Area of Science:

  • Polymer Science
  • Physical Chemistry
  • Computational Chemistry

Background:

  • Polysaccharides like cellulose require specific solvent systems for processing.
  • Ionic liquids (ILs) combined with co-solvents (CSs) are used for polysaccharide dissolution.
  • Understanding solvation behavior in these mixtures is crucial for optimizing dissolution.

Purpose of the Study:

  • To model the solvation of polymers in multi-component solvent mixtures.
  • To associate random walk parameters with chemical interactions and system behavior.
  • To predict the solubility of cellulose in ionic liquid-co-solvent systems.

Main Methods:

  • Application of a multi-walker, discrete-time, discrete-space 1-dimensional random walk model.
  • Fitting a polynomial regression model to analyze system deviations.
  • Analyzing solvent shell interactions and their impact on solubility.

Main Results:

  • The mean number of distinct sites visited in the random walk model correlates with polymer solubility.
  • System behavior is largely determined by available volume and co-solvent molar volume.
  • Deviations from the random walk model indicate specific ionic liquid-co-solvent interactions.

Conclusions:

  • Complex molecular systems can be effectively modeled using a simplified random walk approach.
  • The 1-D random walk model predicts cellulose dissolution ability using minimal experimental data.
  • Specific co-solvents can mediate solubility, offering insights for solvent system design.