Insights into the Multiscale Lubrication Mechanism of Edible Phase Change Materials

Understanding mechanisms behind the oily mouthcoating perception of pure vegetable oils using tribology

Tribology is the science of measuring friction between surfaces. While it has been widely used to investigate texture sensations of food applications, it is seldom applied in pure edible oil systems. In this research, we measured friction, viscosity, and solid fat content (SFC) of nine vegetable oils at 30 and 60°C. Polarized static microscopy was used to assess crystal formation between 60 and 30°C. Descriptive sensory analysis and quantification of oral oil coatings were performed on the oils at 60°C. Expressing the friction factor of oil over the Hersey number (calculated using high sheer-viscosity values) showed no differences in friction between 30 and 60°C, except for shea stearin. Static microscopy revealed crystallization occurred at 30°C for shea stearin, whereas no or few crystals were present for other oils. At 30°C, friction at 1 × 10-2 m/s showed an inverse correlation with SFC (R = -0.95) and with high shear rate viscosity (R = -0.84), as well as an inverse correlation (R = -0.73) with "oily mouthcoating" perception. These results suggest that friction could be a predictor of fat-related perceptions of simple oil systems. Additionally, we hypothesize that the presence of crystals in oils could lower friction via a ball-bearing lubrication mechanism.

Machine learning and topological data analysis identify unique features of human papillae in 3D scans

The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae ([Formula: see text]), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.

A Neural Mechanism in the Human Orbitofrontal Cortex for Preferring High-Fat Foods Based on Oral Texture

Although overconsumption of high-fat foods is a major driver of weight gain, the neural mechanisms that link the oral sensory properties of dietary fat to reward valuation and eating behavior remain unclear. Here we combine novel food-engineering approaches with functional neuroimaging to show that the human orbitofrontal cortex (OFC) translates oral sensations evoked by high-fat foods into subjective economic valuations that guide eating behavior. Male and female volunteers sampled and evaluated nutrient-controlled liquid foods that varied in fat and sugar ("milkshakes"). During oral food processing, OFC activity encoded a specific oral-sensory parameter that mediated the influence of the foods' fat content on reward value: the coefficient of sliding friction. Specifically, OFC responses to foods in the mouth reflected the smooth, oily texture (i.e., mouthfeel) produced by fatty liquids on oral surfaces. Distinct activity patterns in OFC encoded the economic values associated with particular foods, which reflected the subjective integration of sliding friction with other food properties (sugar, fat, viscosity). Critically, neural sensitivity of OFC to oral texture predicted individuals' fat preferences in a naturalistic eating test: individuals whose OFC was more sensitive to fat-related oral texture consumed more fat during ad libitum eating. Our findings suggest that reward systems of the human brain sense dietary fat from oral sliding friction, a mechanical food parameter that likely governs our daily eating experiences by mediating interactions between foods and oral surfaces. These findings identify a specific role for the human OFC in evaluating oral food textures to mediate preference for high-fat foods.SIGNIFICANCE STATEMENT Fat and sugar enhance the reward value of food by imparting a sweet taste and rich mouthfeel but also contribute to overeating and obesity. Here we used a novel food-engineering approach to realistically quantify the physical-mechanical properties of high-fat liquid foods on oral surfaces and used functional neuroimaging while volunteers sampled these foods and placed monetary bids to consume them. We found that a specific area of the brain's reward system, the orbitofrontal cortex, detects the smooth texture of fatty foods in the mouth and links these sensory inputs to economic valuations that guide eating behavior. These findings can inform the design of low-calorie fat-replacement foods that mimic the impact of dietary fat on oral surfaces and neural reward systems.

Out-of-contact peeling caused by elastohydrodynamic deformation during viscous adhesion

We report on viscous adhesion measurements conducted in sphere-plane geometry between a rigid sphere and soft surfaces submerged in silicone oils. Increasing the surface compliance leads to a decrease in the adhesive strength due to elastohydrodynamic deformation of the soft surface during debonding. The force-displacement and fluid film thickness-time data are compared to an elastohydrodynamic model that incorporates the force measuring spring and finds good agreement between the model and data. We calculate the pressure distribution in the fluid and find that, in contrast to debonding from rigid surfaces, the pressure drop is non-monotonic and includes the presence of stagnation points within the fluid film when a soft surface is present. In addition, viscous adhesion in the presence of a soft surface leads to a debonding process that occurs via a peeling front (located at a stagnation point), even in the absence of solid-solid contact. As a result of mass conservation, the elastohydrodynamic deformation of the soft surface during detachment leads to surfaces that come closer as the surfaces are separated. During detachment, there is a region with fluid drainage between the centerpoint and the stagnation point, while there is fluid infusion further out. Understanding and harnessing the coupling between lubrication pressure, elasticity, and surface interactions provides material design strategies for applications such as adhesives, coatings, microsensors, and biomaterials.

Raman Spectroscopy of Emulsions and Emulsion Chemistry

Emulsions are dispersed systems widely used in various industries. In recent years, Raman spectroscopy (RS), as a spectroscopic technique, has gained much attention for measuring and monitoring emulsions. In this review, we explore the use of RS on emulsion structures and emulsification, important reactions that use emulsions such as emulsion polymerization, catalysis and cascading reactions, as well as various applications of emulsions. We explore how RS is used in emulsions, reactions and applications. RS is a powerful and versatile tool for studying emulsions, but there are also challenges in using RS to monitor emulsion processes, especially if they are rapid or volatile. We also explore these challenges and difficulties, as well as possible designs that can be used to overcome them.