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Surface Active Agents

Surface Active Agents

Surfactants, named for their behavior at interfaces, positively adsorb at the interfaces of two phases, reducing interfacial tension. Their versatility as emulsifiers, detergents, and foaming agents stems from this ability. Surfactants, often termed amphiphiles, share the property of amphipathy, with molecules having both hydrophilic and hydrophobic portions. The hydrophilic part is called the head, and the hydrophobic part, including an elongated alkyl substituent, forms the tail.Surfactants...

Micelles

Micelles

Micelle formation is an intricate process that hinges on the properties of amphiphilic or amphipathic molecules and the conditions of the system in which they are found. Amphiphilic molecules, which have both hydrophilic (water-attracting) and hydrophobic (water-repelling) parts, play a critical role in this process.In aqueous environments, these molecules arrange themselves such that their hydrophilic heads are turned towards the water phase, while their hydrophobic tails are oriented away...

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Updated: Jun 20, 2026

Extraction and Characterization of Surfactants from Atmospheric Aerosols

Extraction and Characterization of Surfactants from Atmospheric Aerosols

Published on: April 21, 2017

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks.

Christoforos Brozos^1,2, Jan G Rittig², Sandip Bhattacharya¹

¹BASF Personal Care and Nutrition GmbH, Henkelstrasse 67, 40589 Duesseldorf, Germany.

Journal of Chemical Theory and Computation

|June 26, 2024

Summary

This summary is machine-generated.

This study introduces a graph neural network (GNN) model to predict the temperature-dependent critical micelle concentration (CMC) of surfactants. The model achieves high accuracy, even for unseen surfactants and complex sugar-based molecules.

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Area of Science:

Physical Chemistry
Computational Chemistry
Materials Science

Background:

The critical micelle concentration (CMC) is crucial for surfactant applications.
Existing quantitative structure-property relationship (QSPR) and graph neural network (GNN) models predict CMC at room temperature but neglect temperature dependence.
Temperature-dependent CMC is vital for real-world industrial applications.

Purpose of the Study:

To develop a GNN model for predicting the temperature-dependent CMC of various surfactant classes.
To assess the model's predictive performance across different temperature conditions and surfactant types.
To evaluate the model's generalizability to novel and complex surfactant structures, including sustainable sugar-based surfactants.

Main Methods:

Collected approximately 1400 data points for ionic, nonionic, and zwitterionic surfactants across multiple temperatures from public sources.
Developed and trained a graph neural network (GNN) model to predict temperature-dependent CMC.
Validated the model's predictive quality in two scenarios: with and without prior CMC data for specific surfactants at different temperatures.
Tested the model's performance on sugar-based surfactants with complex molecular structures.

Main Results:

The GNN model achieved high predictive performance with R² ≥ 0.95 on test data in both tested scenarios.
The model demonstrated strong predictive capabilities for generalizing to unseen surfactants.
Model performance was observed to vary across different surfactant classes.
The model successfully predicted the CMC for complex sugar-based surfactants.

Conclusions:

The developed GNN model effectively predicts temperature-dependent CMC for diverse surfactant classes.
The model shows excellent generalizability and accuracy, even for surfactants not included in the training set.
This approach is valuable for predicting the behavior of sustainable surfactants in the personal and home care industries.