Wettability-Engineered Meshes for Gas Microvolume Precision Handling in Liquids

Influence of Surface Wettability on Bubble Formation and Motion

Bubble dynamics plays an important role in boiling heat transfer, and surface wettability affects bubble behaviors. In the present work, the effects of surface superhydrophilicity (SHI) and superhydrophobicity (SHO) on bubble dynamics are experimentally studied by observing the formation and motion behaviors of air bubbles and vapor bubbles on varied surfaces. For air bubbles to better mimic vapor bubbles, the air bubbles are introduced in a water pool by injecting airflow from a through hole of the surface. Air bubble tests are first conducted on homogeneous SHO and SHI surfaces, respectively. It is observed that surface wettability significantly affects the bubble size and departure frequency. To discover the dynamic behaviors of a bubble under both SHI and SHO, a biphilic surface with SHI and SHO areas is fabricated, and air bubbles are injected right on the biphilic border between the two areas. It is observed the wettability contrast significantly displaces the air bubbles, which spread only onto the SHO area. The biphilic surface is fabricated for the pool boiling test. Vapor bubbles are observed at different stages of the nucleate boiling, showing surface effects similar to the observations of air bubbles. Not only does this study present the influence of surface wettability on air and vapor bubble behaviors but also it provides useful implications for understanding and optimizing the biphilic surface design for enhancing boiling heat transfer.

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.

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques.

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.