Scalable Robust Superamphiphobic Coatings Enabled by Self-Similar Structure, Protective Micro-Skeleton, and Adhesive for Practical Anti-Icing of High-Voltage Transmission Tower

Superamphiphobic coatings have wide application prospects in various fields, but suffer from low mechanical robustness. Although some strategies have been reported to solve the issue, it is still challenging for large-scale preparation of robust superamphiphobic coatings via facile approaches. Here, a universal preparation method of mechanically robust superamphiphobic coatings by spraying a suspension containing silica nanoparticles decorated with perfluorodecyl polysiloxane and adhesive microparticles on various substrates have been reported. Phase separation of the adhesive avoids embedding of the nanoparticles in the adhesive by forming the nanoparticles@microparticles structure and meanwhile generates a reentrant tri-tier hierachical micro-/micro-/nanostructure. Phase separation determines micromorphology, chemical composition, superamphiphobicity, and mechanical robustness of the coatings. Consequently, the coatings show excellent static and dynamic superamphiphobicity. Moreover, the coatings show excellent mechanical robustness because of the combined effects of the self-similar structure, protective micro-skeleton, and adhesive. Furthermore, scalable preparation of the suspension and the superamphiphobic coatings have been achieved. The coatings can significantly delay the water freezing time and thus show good passive anti-icing performance on the 1000 kV high-voltage transmission tower. It is believed that the robust superamphiphobic coatings have great application potential, as the method is simple and scalable.

Automated summary

A universal method for preparing mechanically robust superamphiphobic coatings by spraying a suspension containing silica nanoparticles and adhesive microparticles has been reported. The coatings exhibit excellent superamphiphobicity and mechanical robustness, and can significantly delay water freezing.