2026-10-12T01:35:13Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34023349 2026-09-29T11:08:45Z category_146 category_7 category_734 category_931 category_811 category_106 portal_63 item_type_6 month_year_09_2026

Predicting Synergistic and Antagonistic Micellization Effects in Surfactant Blends for Optimized Formulation Design Sofía González-Núñez (22090030) Carlos Amador (22090033) Mariano Martín (1485265) Space Science Medicine Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Science Policy within rubingh ’ showing good agreement scalable computational tool resulting activity coefficients regular solution approximation proposed approach provides interaction parameters derived enabling efficient exploration antagonistic micellization effects scale formulation design computational formulation design surfactant mixture design train predictive models objective optimization strategy dependent β values binary surfactant mixtures surfactant mixtures formulation space surfactant blends typically obtained thermodynamic modeling ternary systems rst ), representative systems reducing reliance predicting synergistic parameter β molecular complexity macroscopic behavior identify formulations experimental parameterization enable end effective quantities component properties chemical space case study balance performance Surfactant mixtures exhibit non-ideal interactions that govern their macroscopic behavior, yet their prediction is limited by the need for experimentally derived interaction parameters. Within Rubingh’s Regular Solution Approximation (RST), these interactions are described by the parameter β, which is typically obtained by fitting experimental mixture data, restricting its applicability in large-scale formulation design. In this work, a computational hybrid approach combining machine learning with thermodynamic modeling is introduced to enable end-to-end prediction and optimization of surfactant mixtures. A dataset of interaction parameters derived from experimental binary surfactant mixtures was constructed to train predictive models for β, together with models for pure-component properties. The predicted, composition-dependent β values are incorporated into the RST framework to estimate mixture behavior without requiring experimental mixture data, while treating the resulting activity coefficients as effective quantities. The overall framework was further validated against a comprehensive dataset of binary surfactant mixtures and further evaluated on ternary systems, showing good agreement with experimental values across diverse interaction regimes and compositions. In addition, a multi-objective optimization strategy is introduced to identify formulations that balance performance and molecular complexity, enabling efficient exploration of chemical space. A case study on representative systems further demonstrates the applicability of the framework for computational formulation design. Overall, the proposed approach provides a scalable computational tool for surfactant mixture design, reducing reliance on experimental parameterization while enabling rapid virtual screening of formulation space. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.jcim.6c02030.s001 https://figshare.com/articles/journal_contribution/Predicting_Synergistic_and_Antagonistic_Micellization_Effects_in_Surfactant_Blends_for_Optimized_Formulation_Design/34023349 CC BY-NC 4.0