@article { , title = {Anti-icing property of bio-inspired micro-structure superhydrophobic surfaces and heat transfer model}, abstract = {Ice accumulation is a thorny problem which may inflict serious damage even disasters in many areas, such as aircraft, power line maintenance, offshore oil platform and locators of ships. Recent researches have shed light on some promising bio-inspired anti-icing strategies to solve this problem. Inspired by typical plant surfaces with super-hydrophobic character such as lotus leaves and rose petals, structured superhydrophobic surface are prepared to discuss the anti-icing property. 7075 Al alloy, an extensively used materials in aircrafts and marine vessels, is employed as the substrates. As-prepared surfaces are acquired by laser processing after being modified by stearic acid for 1 h at room temperature. The surface morphology, chemical composition and wettability are characterized by means of SEM, XPS, Fourier transform infrared (FTIR) spectroscopy and contact angle measurements. The morphologies of structured as-prepared samples include round hump, square protuberance and mountain-range-like structure, and that the as-prepared structured surfaces shows an excellent superhydrophobic property with a WCA as high as 166 ± 2°. Furthermore, the anti-icing property of as-prepared surfaces was tested by a self-established apparatus, and the crystallization process of a cooling water on the sample was recorded. More importantly, we introduced a model to analyze heat transfer process between the droplet and the structured surfaces. This study offers an insight into understanding the heat transfer process of the superhydrophobic surface, so as to further research about its unique property against ice accumulation.}, doi = {10.1016/j.apsusc.2016.12.219}, eissn = {0169-4332}, issn = {0169-4332}, journal = {Applied Surface Science}, note = {12 months embargo OL 03.03.2017}, publicationstatus = {Published}, publisher = {Elsevier}, url = {https://nottingham-repository.worktribe.com/output/853887}, volume = {400}, year = {2017}, author = {Liu, Yan and Li, Xinlin and Jin, Jingfu and Liu, Jiaan and Yan, Yuying and Han, Zhiwu and Ren, Luquan} }