Hypothesis: Jumping of coalesced droplets on superhydrophobic surfaces (SHSs) is widely used for enhanced condensation, anti-icing/frosting, and self-cleaning due to its superior droplet transport capability. However, because only a tiny fraction (about 5%) of the released excess surface energy during coalescence can be transformed into jumping kinetic energy, the jumping is very weak, limiting its application.
Methods: We experimentally propose enhanced jumping methods, use machine learning to design structures that achieve ultimate jumping, and finally combine experiments and simulations to investigate the mechanism of the enhanced jumping.
Finding: We find that a more orderly flow inside the droplets through the structure is the key to improve energy transfer efficiency and that the egg tray-like structure enables the droplet to jump with an energy transfer efficiency 10.6 times higher than that of jumping on flat surfaces. This energy transfer efficiency is very close to the theoretical limit, i.e., almost all the released excess surface energy is transformed into jumping kinetic energy after overcoming viscous dissipation. The ultimate jumping enhances the application of water droplet jumping and enables other low surface energy fluid such as R22, R134a, Gasoline, and Ethanol, which cannot jump on a flat surface, to jump.
Keywords: ADAM; Coalescence-induced droplet jumping; Energy transfer efficiency; Enhanced jumping; Machine learning; Superhydrophobic surface.
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