Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks-Reference-Cited by-同舟云学术

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Published:2023-04-24 Issue:18 Volume:39 Page:6406-6412
ISSN:0743-7463
Container-title:Langmuir
language:en
Short-container-title:Langmuir

Author:

Wang Li¹²,Wang Haidou¹³^ORCID,Di Yuelan²,Dong Lihong²,Jin Guo¹

Affiliation:

1. College of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150090, Heilongjiang, China

2. Key Laboratory of National Defense Science and Technology for Equipment Remanufacturing Technology, Army Armored Forces Academy, Beijing 100072, China

3. National Engineering Research Center for Remanufacturing, Army Armored Forces Academy, Beijing 100072, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Electrochemistry,Spectroscopy,Surfaces and Interfaces,Condensed Matter Physics,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.langmuir.3c00188

Reference39 articles.

1. Reentrant liquid condensate phase of proteins is stabilized by hydrophobic and non-ionic interactions

2. Functional map of biological and biomimetic materials with hierarchical surface structures

3. Theoretical Explanation of the Lotus Effect: Superhydrophobic Property Changes by Removal of Nanostructures from the Surface of a Lotus Leaf

4. Fly-Eye Inspired Superhydrophobic Anti-Fogging Inorganic Nanostructures

5. Anti-icing performance of superhydrophobic surfaces

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective;IEEE Access;2024