Superhydrophobic Surface Designing for Efficient Atmospheric Water Harvesting Aided by Intelligent Computer Vision
Hok Yin Wong, Lok Wing Wong, Chi Sing Tsang, Zhangyuan Yan, Xuming Zhang, Jiong Zhao, Thuc Hue Ly
Abstract
Atmospheric water harvesting (AWH) is a possible solution for the current water crisis on the Earth, and the key process of AWH has been widely applied in commercial dehumidifiers. To improve the energy efficiency of the AWH process, applying a superhydrophobic surface to trigger coalescence-induced jumping could be a promising technique that has attracted extensive interest. While most previous studies focused on optimizing the geometric parameters such as nanoscale surface roughness (<1 μm) or microscale structures (10 μm to a few hundred μm range), which might enhance AWH, here, we report a simple and low-cost approach for superhydrophobic surface engineering, through alkaline oxidation of copper. The prepared medium-sized microflower structures (3-5 μm) by our method could fill the gap of the conventional nano- and microstructures, simultaneously act as the preferable nucleation sites and the promoter for the condensed droplet mobility including droplet coalescence and departure, and eventually benefit the entire AWH performances. Moreover, our AWH structure has been optimized with the aid of machine learning computer vision techniques for droplet dynamic analysis on a micrometer scale. Overall, the alkaline surface oxidation and medium-scale microstructures could provide excellent opportunities for superhydrophobic surfaces for future AWH.