A new methodology for measuring solid/liquid interfacial energy.

HYPOTHESIS: The interfacial energy γsl between a solid and a liquid designates the affinity between these two phases, and in turn, the macroscopic wettability of the surface by the fluid. This property is needed for precise control of fluid-transport phenomena that affect the operation/quality of commercial devices/products. Although several indirect or theoretical approaches can quantify the solid/liquid interfacial energy, no direct experimental procedure exists to measure this property for realistic (i.e. rough) surfaces. Makkonen hypothesized that the frictional resistance force per unit contact-line length is equal to the interfacial energy on smooth surfaces, which, however, are rarely found in practice. Consequently, the hypothesis that Makkonen's assumption may also hold for rough surfaces (which are far more common in practice) arises naturally. If so, a reliable and simple experimental methodology of obtaining γsl for rough surfaces can be put forth. This is accomplished by performing dynamic contact-angle experiments on rough surfaces that quantify the relationship between the frictional resistance force per unit contact-line length acting on an advancing liquid (Fp,a) and the surface roughness in wetting configurations. EXPERIMENT: We perform static and advancing contact-line experiments with aqueous and organic liquids on different hydrophilic surfaces (Al, Cu, Si) with varying Wenzel roughnesses in the range 1-2. These parameters are combined with the liquid's known surface tension to determine Fp,a. FINDINGS: Fp,a rises linearly with the surface roughness. Analysis based on existing theories of wetting and contact-angle hysteresis reveals that the slope of Fp,a vs.Wenzel roughness is equal to the solid/liquid interfacial energy, which is thus determined experimentally with the present measurements. Interfacial energies obtained with this experimental approach are within 12% of theoretically predicted values for several solid/liquid pairs, thereby validating this methodology.

Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure.

Surfaces with extreme wettability (too low, superhydrophobic; too high, superhydrophilic) have attracted considerable attention over the past two decades. Titanium dioxide (TiO2) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO2 with ethanol and a commercial fluoroacrylic copolymer dispersion, known as PMC, can produce coatings with water contact angles approaching 170°. Another property of interest for this specific TiO2 formulation is its photocatalytic behavior, which causes the contact angle of water to be gradually reduced with rising timed exposure to UV light. While this formulation has been employed in many studies, there exists no quantitative guidance to determine or tune the contact angle (and thus wettability) with the composition of the coating and UV exposure time. In this article, machine learning models are employed to predict the required UV exposure time for any specified TiO2/PMC coating composition to attain a certain wettability (UV-reduced contact angle). For that purpose, eight different coating compositions were applied to glass slides and exposed to UV light for different time intervals. The collected contact-angle data was supplied to different regression models to designate the best method to predict the required UV exposure time for a prespecified wettability. Two types of machine learning models were used: (1) parametric and (2) nonparametric. The results showed a nonlinear behavior between the coating formulation and its contact angle attained after timed UV exposure. Nonparametric methods showed high accuracy and stability with general regression neural network (GRNN) performing best with an accuracy of 0.971, 0.977, and 0.933 on the test, train, and unseen data set, respectively. The present study not only provides quantitative guidance for producing coatings of specified wettability, but also presents a generalized methodology that could be employed for other functional coatings in technological applications requiring precise fluid/surface interactions.

Lateral Spreading of Gas Bubbles on Submerged Wettability-Confined Tracks.

Spreading of liquid droplets on wettability-confined paths has attracted considerable attention in the past decade. On the other hand, the inverse scenario of a gas bubble spreading on a submerged, wettability-confined track has rarely been studied. In the present work, an experimental investigation of the spreading of millimetric gas bubbles on horizontally submerged, textured, wettability-confined tracks is carried out. The width of the track is kept fixed along its entire length, and the spreading behavior of a gas bubble, dispensed at one end of the track, is studied. The effects of varying track width, bubble diameter, and ambient liquid are investigated. Post-contact, the gas bubble spreads along the track at a linear rate with time, while remaining pinned at its back end; the recorded spreading speed is O(0.5 m/s). An inertio-capillary force balance describes the experimentally observed spreading dynamics with excellent agreement.

Wettability-Engineered Meshes for Gas Microvolume Precision Handling in Liquids.

The interaction of rising gas bubbles with submerged air-repelling or air-attracting surfaces is relevant to various technological applications that rely on gas-microvolume handling or removal. This work demonstrates how submerged metal meshes with super air-attracting/repelling properties can be employed to manipulate microvolumes of air, rising buoyantly in the form of bubbles in water. Superaerophobic meshes are observed to selectively allow the passage of air bubbles depending on the mesh pore size, the bubble volume-equivalent diameter, and the bubble impact velocity on the mesh. On the other hand, superaerophilic meshes reduce or amplify the volume captured from a train of incoming bubbles. Finally, a spatial wettability pattern on the mesh is used to control the size of the outgoing bubble, and an empirical relation is formulated to predict the released gas volume. The study demonstrates how porous materials with controlled wettability can be used to precisely modulate and control the outcome of bubble/mesh interactions.