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Multicomponent ionic liquid CMC prediction.

I E Kłosowska-Chomiczewska1, W Artichowicz, U Preiss

  • 1Department of Colloid and Lipid Science, Faculty of Chemistry, Gdańsk University of Technology, Narutowicza St. 11/12, Gdańsk 80-233, Poland. christian.jungnickel@pg.gda.pl.

Physical Chemistry Chemical Physics : PCCP
|September 16, 2017
PubMed
Summary

We developed a predictive model for the critical micelle concentration (CMC) of ionic liquids (ILs) using machine learning. The model accurately predicts CMC in various systems, including those with salts or alcohols.

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Area of Science:

  • Physical Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Predicting the critical micelle concentration (CMC) of ionic liquids (ILs) is crucial for their application in various chemical processes.
  • Existing models often have limitations in scope, typically focusing on simplified binary systems and a restricted number of ILs.

Purpose of the Study:

  • To develop and validate a robust predictive model for the CMC of ILs across diverse chemical environments.
  • To identify key molecular descriptors influencing IL micellization in binary and ternary systems, including the presence of additives like salts and alcohols.

Main Methods:

  • Utilized a dataset of 704 experimental CMC values from 43 publications.
  • Employed Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) regression methodologies.
  • Selected descriptors included IL molecular volume (Vm), solvent-accessible surface (Ŝ), solvation enthalpy (ΔsolvG∞), and concentrations/volumes of salts (Cs, Vms) or alcohols (Ca, Vma).
  • Data was split (80/20) into training and validation sets, with bootstrap aggregation applied.

Main Results:

  • The KSVM model achieved a higher predictive accuracy with an average R² of 0.843 and Mean Squared Error (MSE) of 0.608.
  • The EA model yielded an R² of 0.794 and MSE of 0.973.
  • Sensitivity analysis indicated that IL molecular volume (Vm) and solvent-accessible surface (Ŝ) are primary drivers of micellization.
  • Surprisingly, Vm's influence diminished in the presence of alcohol, highlighting the role of additives in modifying micelle formation.

Conclusions:

  • The developed KSVM model demonstrates a significant advancement in predicting IL CMC across a wider range of complex systems.
  • The study reveals the nuanced impact of molecular descriptors and additives on IL micellization behavior.
  • This work provides a valuable tool for designing and optimizing IL-based systems for various applications.