Pickering emulsions revisited: interfacial stability, particle geometry, and emerging predictive strategies

Pickering emulsions stabilized by solid particles present unique formulation challenges due to the complex interplay of particle properties, interfacial dynamics, and processing conditions. This review revisits the fundamental physics governing Pickering emulsions, including particle adsorption energetics, wettability, geometry effects, interfacial rheology, and instability mechanisms, while examining how these factors influence emulsion stability and functionality across applications in food, pharmaceuticals, and materials science. Classical analytical models and simulation techniques (MD, DPD, LBM) are discussed alongside their assumptions and limitations. We then assess how artificial intelligence and machine learning methods, developed primarily in broader emulsion science, may be adapted to particle-stabilized systems. By critically linking physical mechanisms with emerging predictive strategies, this perspective highlights both the current capabilities and limitations of modeling approaches in addressing the inherently multiscale nature of Pickering emulsions. Particular emphasis is placed on identifying challenges that are specific to particle-stabilized (Pickering) systems, including multiscale coupling between particle geometry, interfacial structure, and droplet stability, which remain difficult to address using existing theoretical and data-driven approaches. Graphical abstract