Emulsions form the basis of products used by millions of people in their day-to-day life. Despite the large use of emulsion-based products (e.g. cosmetics, paint, food and pharmaceuticals), formulating emulsions is time consuming and expensive, often resorting to trial-and-error approaches. Reliable first-principles methods for prediction of the entire phase diagram are lacking, because of the complexity of the system. Predictive methods exist, but are often too computationally intensive to cover the whole parameter space, like molecular dynamics-based methods, or too inaccurate for emulsions, like group contribution methods.
In this project, we aim to develop a fast and robust method for predicting full emulsion phase diagrams based on density functional theory and the COSMO-RS implicit solvent model, allowing prediction of emulsion properties from first-principles. A schematic composition-temperature phase diagram (often called a fish diagram) is shown in Figure 1.
Our proposed method is based on being able to predict the thermodynamic multiphase liquid-liquid equilibria, including the interfacial tension and interfacial composition, which taken together is able to describe partitioning of surfactant into oil or water and the overall behaviour of the mixture, such as the presence of an oil-in-water emulsion, a water-in-oil emulsion or a stable microemulsion.
Our goal is that this tool will be accurate enough to significantly shorten the time to find the optimal composition for a desired emulsion formulation, which could vastly improve the efficiency of formulated product development in a range of industrial settings.