The ferro-liquid droplet manipulation on hydrophobic surfaces remains vital for various applications in biomedicine, sensors and actuators, and oil–water separation. The magnetic influence of ferro-liquid droplets on the hydrophobic surface is elucidated. The mechanisms of a newborn droplet formation under the magnetic force are explored. The sliding and rolling dynamics of the ferro-liquid droplets are assessed for the various concentrations wt % of ferro-particles. High-speed recording and a tracker program are used to evaluate the droplet sliding and translational velocities. It is demonstrated that the mode of droplet motion changes from sliding to rolling as the magnetic Bond number increases, in which case, the droplet position becomes close to the magnet surface. The translational velocity of the droplet under rolling mode increases as the ferro-particle concentration in the droplet fluid increases. A further increase of the magnetic Bond number results in the creation of a newborn droplet attached to the magnet surface.

Impacting droplets and droplet ejection from hydrophobic mesh surfaces have interest in biomedicine, heat transfer engineering, and self-cleaning of surfaces. The rate and the size of newborn droplets can vary depending on, the droplet fluid properties, Weber number, mesh geometry, and surface wetting states. In the present study, impacting water droplets onto hydrophobic mesh surface is investigated and impact properties including, spreading, rebounding, and droplet fluid penetration and ejection rates are examined. Droplet behavior is assessed using high recording facilities and predicted in line with the experiments. The findings reveal that the critical Weber number for droplet fluid penetrating/ejecting from mesh screen mainly depends on the droplet fluid capillary length, and hydrophobic mesh size. The contact time of impacting droplet over mesh surface reduces with increasing droplet Weber number, which opposes the case observed for impacting droplets over flat hydrophobic surfaces. The restitution coefficient attains lower values for impacting droplets over mesh surfaces than that of flat surfaces. The rate and diameter of the ejected droplet from the mesh increases as droplet Weber increases. At the onset of impact, streamline curvature is formed inside droplet fluid, which creates a stagnation zone with radially varying pressure at the droplet fluid mesh interface. This reduces the ejected droplet diameter from mesh cells as mesh cells are located away from the impacting vertical axis.

Machine learning (ML) techniques are used to predict the coefficient of friction of an epoxy polymer resin (SU-8) and its composite coatings deposited on a silicon wafer. Filler type and the number of cycles are taken as the input parameters. The filler types included, two solid fillers namely, graphite and talc, and a liquid filler such as Perfluoropolyether (PFPE). Six variations of the SU8 coatings were developed based on the different combinations of filers used and tested. The experimental data generated for these different coatings for varying number of cycles (0 to 499) was used to train the different ML algorithms like ANN, SVM, CART, and RF to predict the coefficient of friction. The performance of these ML techniques was compared by calculating mean absolute error (MAE), root means square error (RMSE), and square of the correlation coefficient (R2). The ANN algorithm was observed to have the best (R2) metrics while the other ML techniques SVM, CART, and RF had a satisfactory performance with some inaccuracies seen for the CART algorithm for the data set under consideration.

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Rolling liquid droplets is of great interest for various applications including self-cleaning of surfaces. Interfacial resistance, in terms of pinning and shear rate, has a critical role in droplet rolling...