Tribology of dual Pickering double emulsions: Machine learning-aided inner droplet analysis

This study investigated the tribological performance of Pickering water-in-oil emulsions and dual Pickering water-in-oil-in-water double emulsions (DEs) stabilized with particles at both the interfaces. W/O emulsions were stabilized by cocoa butter-based oleogel (CBolg) crystals, while DEs incorporating these emulsions were stabilized by whey protein microgels (WPM). The influence of temperature (21 and 37 °C) and surface texture (smooth vs biomimetic tongue-like surface) were investigated in tribology of W/O emulsions (30–60 % v/v water) and DEs (with 20 and 60 wt% W/O phase). In smooth surfaces, CBolg played a critical role in reducing the friction coefficient (μ) primarily via a fat-driven lubrication mechanism that was temperature dependent. While in DEs, smaller oil droplets encapsulating water provided similar lubrication to oil-based systems until starvation occurred. Strikingly, the water content in W/O emulsions exhibited distinct differences between emulsion systems within the biomimetic tongue-like surfaces, demonstrating lower lubricity at higher water concentration. Confocal microscopy images analyzed using Machine Learning (ML)-supported droplet segmentation enabled a more precise evaluation of structural changes within DEs when subjected to tribological stress. We demonstrated that although changes in inner droplet size altered in DEs, their contribution to the overall lubrication performance was minimal, due to their limited entrainment. Of more importance, the tribological performance was governed by the WPM with minimal influence from the droplet-entrained phenomena. These fundamental insights highlight the relevance of structured water in understanding frictional performance in emulsified systems, with structural integrity, composition, and topography of the tribological surface emerging as key factors.