Photothermal superhydrophobic copper nanowire assemblies: fabrication and deicing/defrosting applications
Source abstract
Abstract
Abstract Ice and frost buildup continuously pose significant challenges to multiple fields. As a promising de-icing/defrosting alternative, designing photothermal coatings that leverage on the abundant sunlight source on the earth to facilitate ice/frost melting has attracted tremendous attention recently. However, previous designs suffered from either localized surface heating owing to the limited thermal conductivity or unsatisfied meltwater removal rate due to strong water/substrate interaction. Herein, we developed a facile approach to fabricate surfaces that combine photothermal, heat-conducting, and superhydrophobic properties into one to achieve efficient de-icing and defrosting. Featuring copper nanowire assemblies, such surfaces were fabricated via the simple template-assisted electrodeposition method, allowing us to tune the nanowire assembly geometry by adjusting the template dimensions and electrodeposition time. The highly ordered copper nanowire assemblies facilitated efficient sunlight absorption and lateral heat spreading, resulting in a fast overall temperature rise to enable the thawing of ice and frost. Further promoted by the excellent water repellency of the surface, the thawed ice and frost could be spontaneously and promptly removed. In this way, the all-in-one design enabled highly enhanced de-icing and defrosting performance compared to other nanostructured surfaces merely with superhydrophobicity, photothermal effect, or the combination of both. In particular, the defrosting efficiency could approach ∼100%, which was the highest compared to previous studies. Overall, our approach demonstrates a promising path toward designing highly effective artificial deicing/defrosting surfaces.
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Research context
OpenAlex topics: Surface Modification and Superhydrophobicity · Icing and De-icing Technologies · Thermal Radiation and Cooling Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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