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 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 crystal-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, evidencing lower lubricity at higher water concentration. Confocal microscopy images analyzed supported by 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. • Fat crystals in W/O emulsions reduce the friction, regardless of the water content • The internal structure of the W/O/W emulsions (DEs) affects boundary lubrication • ML-based image analysis provides inner droplet size, before and post-tribology • Larger droplet size of DEs limit entrainment in soft and biomimetic tongue surfaces • Biomimetic tongue surface pinpoints frictional differences between emulsions

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Publication Details

Journal
Food Hydrocolloids
Published
2025-09-27
DOI
https://doi.org/10.1016/j.foodhyd.2025.112035
Citations
1
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
2.48
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article

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

1 citations
Food Hydrocolloids
Geochemistry and Geologic Mapping
2.48
article

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

article en
1 citations

Abstract

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 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 crystal-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, evidencing lower lubricity at higher water concentration. Confocal microscopy images analyzed supported by 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. • Fat crystals in W/O emulsions reduce the friction, regardless of the water content • The internal structure of the W/O/W emulsions (DEs) affects boundary lubrication • ML-based image analysis provides inner droplet size, before and post-tribology • Larger droplet size of DEs limit entrainment in soft and biomimetic tongue surfaces • Biomimetic tongue surface pinpoints frictional differences between emulsions

Food HydrocolloidsVol. 172
University of Copenhagen (DK), University of Leeds (GB), Politecnico di Torino (IT), University of Southern Denmark (DK)
Openalex Percentile: Top 7%
Geochemistry and Geologic Mapping
2.48
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