Jet Impact on Superhydrophobic Metal Mesh.

Liquid-jet impact on porous, relatively thin solids has a variety of applications in heat transfer, filtration, liquid-fuel atomization, incontinence products, and solid-substrate erosion, among others. Many prior studies focused on liquid-jet impact on impermeable substrates, and some have investigated the hydraulic jump phenomenon. In the present work, the liquid jet strikes a superhydrophobic, permeable, metal mesh orthogonally, and the radial spreading and throughflow of the liquid are characterized. The prebreakthrough hydraulic jump, the breakthrough velocity, and the postbreakthrough spatial distributions of the liquid are investigated by varying the liquid properties (density, surface tension, and viscosity) and the openness of the metal mesh. The hydraulic jump radius in the prebreakthrough regime increases with jet velocity and is independent of the liquid properties and mesh geometry (pore size, wire diameter and pitch). The breakthrough velocity increases with surface tension of the liquid and decreases with the mesh opening diameter and liquid viscosity. A simple analytical model predicts the jet breakthrough velocity; its predictions are in accordance with the experimental observations. In the postbreakthrough regime, as the jet velocity increases, the liquid flow rate penetrating the mesh shows an initially steep increase, followed by a plateau, which is attributed to a Cassie-Baxter-to-Wenzel transition at the impact area of the mesh.

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.

Revisiting the supplementary relationship of dynamic contact angles measured by sessile-droplet and captive-bubble methods: Role of surface roughness.

HYPOTHESIS: Quantitative characterization of surface wettability through contact angle (CA) measurement using the sessile droplet (SD) or captive bubble (CB) methods is often limited by the intrinsic wetting properties of the substrate. Situations may arise when an extreme surface wettability may preclude using one of the two methods for predicting the behaviors of droplets or bubbles on the surface. This warrants a relationship between the dynamic CAs measured via the SD and CB methods. While the two dynamic CAs (e.g., the advancing CA of SD and receding CA of CB) add up to 180° on a smooth surface, the simple geometric supplementary principle may not apply for rough surfaces. EXPERIMENTS: We perform a systematic wettability characterization of solid substrates with varying degrees of roughness using the sessile-droplet and captive-bubble methods, and interpret the experimental observations using a theoretical model. FINDINGS: The dynamic contact angles measured by the sessile-droplet and captive-bubble methods deviate from the supplementary principle as the surface roughness is increased. We present a theoretical explanation for this disparity and predict the values of the contact angles using prevalent thermodynamic models of wetting and contact-angle hysteresis on rough substrates. The theoretical prediction is in good agreement with the experimental observations.