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While ridged, spherical, or cone superhydrophobic surfaces have been extensively utilized to explore the droplet impact dynamics and the possibility of reducing contact time, superhydrophobic surfaces with a single small pillar have received less attention. Here, we report the rebound and splashing phenomena of impact droplets on various single-pillar superhydrophobic surfaces with the pillars having smaller or equal sizes compared to the droplets. Our results indicate that the single-pillar superhydrophobic surfaces inhibit the droplet splashing compared to the flat ones, and the rebound droplets on the former sequentially exhibit three morphologies of top, bottom, and breakup rebounds with the increasing of Weber number, while those on the latter only show the (bottom) rebound. The pillar significantly enlarges the droplet spreading factor but hardly changes the droplet width. Both the relations between the maximum spreading and width factors and the Weber number on all surfaces approximately follow a classical 1/4-power law. Reduction in the contact time is observed for the rebound droplets on the single-pillar superhydrophobic surfaces, dependent on the rebound morphology. Specially, the breakup rebound nearly shortens the contact time by more than 50% with a larger pillar-to-droplet diameter ratio yielding a greater reduction. We provide scaling analyses to demonstrate that this remarkable reduction is ascribed to the decrease in the volume of each sub-droplet after breakup. Our experimental investigation and theoretical analysis provide insight into the droplet impact dynamics on single-pillar superhydrophobic surfaces.

The jumping direction is an essential characteristic of jumping droplets, but it is poorly understood and uncontrollable at present. In this work, we present a method to control the jumping direction by surface structures, where the jumping direction is controlled by changing the inclination angle of the structure. The underlying mechanism is analyzed experimentally, with numerical simulations, and using a theoretical model developed to relate the jumping direction and the inclination angle for a few cases with a specific distribution. Because random droplet distributions are more common on actual condensation surfaces, a more comprehensive prediction model was developed based on a convolution neural network (CNN) to predict the jumping direction for more general cases. The input to the CNN is an image of droplets with various distribution features, which are detected by the neural network and used to predict the jumping angle. SHapley Additive exPlanations methods were then used to analyze the feature importance and to give human-understandable insights from the prediction model. This work offers avenues for improving cooling rates, anti-icing/freezing characteristics, and self-cleaning attributes and contributes to a better understanding of the jumping direction.

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