Design of Surfactant Molecules Under Performance Constraints.

The industrial applications of surfactant solutions are both numerous and extremely diverse, demonstrating the importance of these systems in everyday life and driving the need for a systematic approach to designing sustainable surfactant molecules adapted to the specific requirements of each application. Given the very large space of possible molecules, the identification of candidate surfactants that achieve a balance between the optimal physicochemical properties of the product and minimal environmental and health impacts is extremely challenging. In this work, a formulation and solution framework based on Computer-Aided Molecular Design is proposed for surfactant design. A novel multistage methodology is developed based on the initial generation of promising candidates for the two constituents of a surfactant, the hydrophilic head and the hydrophobic tail, followed by the multiobjective optimization of surfactant molecules. This decomposition results in an effective solution strategy. In addition to constraints that ensure the generation of feasible molecules, specific structural constraints can be incorporated in the formulation, accelerating the discovery and optimization process. Data-driven predictive models for the most relevant surfactant properties, such as critical micelle concentration, Krafft point, surface tension, toxicity, and biodegradability, are developed and implemented in the optimization formulation. Two case studies are tackled, successfully generating novel surfactant molecules. The proposed framework could be extended to more complex structures, such as two-headed or Gemini surfactants.