کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7835608 | 1503535 | 2018 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A significant reduction of ice adhesion on nanostructured surfaces that consist of an array of single-walled carbon nanotubes: A molecular dynamics simulation study
ترجمه فارسی عنوان
کاهش قابل توجهی از چسبندگی یخ در سطوح نانوساختار که شامل مجموعه ای از نانولوله های کربنی تک محور است: یک مطالعه شبیه سازی دینامیکی مولکولی
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کلمات کلیدی
کاهش چسبندگی یخ، سطوح نانوساختار، دینامیک مولکولی،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
چکیده انگلیسی
It is well recognized that excessive ice accumulation at low-temperature conditions can cause significant damage to civil infrastructure. The passive anti-icing surfaces provide a promising solution to suppress ice nucleation and enhance ice removal. However, despite extensive efforts, it remains a challenge to design anti-icing surfaces with low ice adhesion. Using all-atom molecular dynamics (MD) simulations, we show that surfaces with single-walled carbon nanotube array (CNTA) significantly reduce ice adhesion due to the extremely low solid areal fraction. It was found that the CNTA surface exhibits up to a 45% decrease in the ice adhesion strength in comparison with the atomically smooth graphene surface. The details of the ice detachment from the CNTA surface were examined for different water-carbon interaction energies and temperatures of the ice cube. Remarkably, the results of MD simulations demonstrate that the ice detaching strength depends linearly on the ratio of the ice-surface interaction energy and the ice temperature. These results open the possibility for designing novel robust surfaces with low ice adhesion for passive anti-icing applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Surface Science - Volume 437, 15 April 2018, Pages 202-208
Journal: Applied Surface Science - Volume 437, 15 April 2018, Pages 202-208
نویسندگان
Luyao Bao, Zhaoyuan Huang, Nikolai V. Priezjev, Shaoqiang Chen, Kai Luo, Haibao Hu,