First-principles prediction of critical micellar concentrations for ionic and nonionic surfactants

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Materials Science

Background:

• The critical micelle concentration (CMC) is a fundamental property governing surfactant behavior in solutions.
• Accurate CMC prediction is crucial for effective formulation design in various industries.
• Experimental determination of CMC involves surface tension measurements, which can be time-consuming.

Purpose of the Study:

• To develop and validate a novel computational method for predicting the critical micelle concentration (CMC) of diverse surfactants.
• To establish a first-principles based approach for in-silico prediction of surfactant behavior.
• To enable accurate formulation design through reliable CMC predictions.

Main Methods:

• Utilized COSMO-RS theory for first-principles based interfacial tension calculations.
• Developed a hybrid prediction strategy combining micelle formation and phase separation models.
• Applied the method to predict CMC for nonionic, cationic, anionic, and zwitterionic surfactants in water.

Main Results:

• Achieved accurate predictions for the critical micelle concentration (CMC) across a wide range of surfactant types.
• Predictions were within one log unit of experimental values.
• The method successfully accounts for varying hydrophilic headgroup polarities.

Conclusions:

• The developed computational method provides accurate, first-principles based predictions of surfactant CMC.
• This approach offers a powerful tool for in-silico formulation design and property prediction.
• The method paves the way for predicting properties of more complex surfactant systems.