Quantitative investigation of surfactant monolayer bending tendency at an oil-polar solvent interface using DPD modeling and artificial neural networks.

Abstract

The bending tendency of a surfactant monolayer at an interface is critical in determining the type of emulsion formed and the proximity of the emulsion system to its equilibrium state. Despite its importance, the influence of interaction and surfactant structure on the bending tendency has not been quantitatively investigated. In this study, we develop and validate an artificial neural network (ANN) model based on the torque densities from dissipative particle dynamics (DPD) simulations to address this gap. With the validated ANN model, the relationship between surfactant monolayer bending tendency and all the interaction parameters, oil size, and surfactant structure (size and tail branching) was derived, from which the significance of each factor was ranked. With this ANN model, both the relationship and factor analysis can be instantly investigated without further DPD modeling. Furthermore, we expand the study to surfactant-oil-polar solvent (SOP) systems by varying the interaction parameters between polar solvents (PP). Our finding indicates that the interaction between polar solvents plays an important role in determining the bending tendency of surfactant monolayers; weaker intermolecular attraction between polar solvents makes surfactants tend to bend toward the oil phase (tend to form oil in polar solvent emulsion). Factor analysis reveals that increasing the repulsion between head-head (HH) or head-oil (HO) makes the model surfactants more polar-solvophilic, while increasing the repulsion between polar solvent-head (PH), tail-tail (TT) or oil-oil (OO) makes the model surfactants more lipophilic. The ANN model effectively reproduces the dependence of surfactant monolayer bending tendency on oil size, consistent with experimental observations, the larger the oil size, the higher the bending tendency toward the oil phase. The most intriguing insight derived from the ANN model here is that the effect of branching in the lipophilic tail will be enhanced by factors that make surfactants behave more lipophilic in a surfactant-oil-polar solvent (SOP) system, for rather polar-solvophilic surfactants, the effect of tail branching is negligible.