Fog Collection Efficiency of Superhydrophobic Surfaces with Different Water Adhesion Prepared by Laser Grid Texturing

22 Pages Posted: 30 Sep 2023

See all articles by Shufan Liu

Shufan Liu

Soochow University

Chengfeng Sun

Soochow University

Kedong Zhang

Soochow University

Yan Geng

Soochow University

Dedong Yu

affiliation not provided to SSRN

Chengdong Wang

Soochow University

Abstract

Superhydrophobic surfaces are crucial in various applications due to their unique wettability. Inspired by the fog collection mechanism of the Namib beetle, numerous researchers have focused on preparing similar hybrid superhydrophilic-superhydrophobic surfaces to improve the fog collection efficiency. However, it is essential to understand how water adhesion affects the efficiency of fog collection on superhydrophobic surfaces, which can help design more scientific surfaces for fog collection. This paper first prepared superhydrophobic titanium alloy surfaces with different water adhesions by adjusting the laser grid texturing interval combined with simple silicone oil heat treatment. The differences in surface adhesion were verified by measuring the sliding angle, contact angle hysteresis, and the droplet bouncing experiments. The fog collection experiment indicated that specimens with the texturing interval of 100 µm had the highest fog collection efficiency due to the appropriate surface adhesion effectively suppressing the coalescence-induced droplet jumping phenomenon and changing the detachment mode of the condensed droplets. Additionally, lower water adhesion facilitates the removal of condensed droplets and improves droplet mobility on the surfaces.

Keywords: LASER PROCESSING, superhydrophobic surface, Water adhesion regulation, fog collection

Suggested Citation

Liu, Shufan and Sun, Chengfeng and Zhang, Kedong and Geng, Yan and Yu, Dedong and Wang, Chengdong, Fog Collection Efficiency of Superhydrophobic Surfaces with Different Water Adhesion Prepared by Laser Grid Texturing. Available at SSRN: https://ssrn.com/abstract=4588568 or http://dx.doi.org/10.2139/ssrn.4588568

Shufan Liu

Soochow University ( email )

No. 1 Shizi Street
Taipei, 215006
Taiwan

Chengfeng Sun

Soochow University ( email )

No. 1 Shizi Street
Taipei, 215006
Taiwan

Kedong Zhang

Soochow University ( email )

No. 1 Shizi Street
Taipei, 215006
Taiwan

Yan Geng

Soochow University ( email )

No. 1 Shizi Street
Taipei, 215006
Taiwan

Dedong Yu

affiliation not provided to SSRN ( email )

Chengdong Wang (Contact Author)

Soochow University ( email )

No. 1 Shizi Street
Taipei, 215006
Taiwan

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
28
Abstract Views
141
PlumX Metrics