Design of Surfactant Molecules Under Performance Constraints

Area of Science:

• Materials Science
• Computational Chemistry
• Sustainable Chemistry

Background:

• Surfactant solutions are vital in numerous industrial applications, necessitating sustainable molecule design.
• Identifying surfactants balancing performance with minimal environmental impact is complex due to vast molecular possibilities.

Purpose of the Study:

• To propose a computational framework for designing sustainable surfactant molecules.
• To develop a systematic approach for optimizing surfactant properties and minimizing environmental impact.

Main Methods:

• Utilized Computer-Aided Molecular Design (CAMD) for surfactant formulation and solution.
• Developed a multistage methodology involving candidate generation for hydrophilic/hydrophobic parts and multiobjective optimization.
• Integrated data-driven predictive models for key properties like critical micelle concentration, Krafft point, surface tension, toxicity, and biodegradability.

Main Results:

• Successfully generated novel surfactant molecules through the developed framework.
• Demonstrated the framework's effectiveness in two case studies.
• Showcased the ability to incorporate specific structural constraints for accelerated discovery.

Conclusions:

• The proposed CAMD framework offers an effective strategy for designing sustainable surfactants.
• The methodology can be extended to more complex surfactant structures, including Gemini surfactants.
• This approach facilitates the balance between desired physicochemical properties and reduced environmental/health concerns.