Robust Superamphiphobic Coating Applied to Grease-Proof Mining Transformer Components-Reference-Cited by-同舟云学术

Robust Superamphiphobic Coating Applied to Grease-Proof Mining Transformer Components

Published:2023-05-25 Issue:22 Volume:39 Page:7968-7978
ISSN:0743-7463
Container-title:Langmuir
language:en
Short-container-title:Langmuir

Author:

Zhang Jiaxu¹²,Zhu Lina¹³,Wang Chengbiao¹,Huang Jinxia²,Guo Zhiguang⁴²^ORCID

Affiliation:

1. School of Engineering and Technology, China University of Geosciences, Beijing 100083, People’s Republic of China

2. State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, People’s Republic of China

3. Zhengzhou Institute, China University of Geosciences, Beijing 450000, Zhengzhou, People’s Republic of China

4. Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei University, Wuhan 430062, People’s Republic of China

Funder

National Key Research and Development Program of China

Pre-Research Program in National 14th Five-Year Plan

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.3c00858

Reference65 articles.

1. Statistical Evaluation of Machining Parameters in Drilling of Glass Laminate Aluminum Reinforced Epoxy Composites using Machine Learning Model

2. Experimental and Statistical Evaluation of Drilling induced Damages in GFRP Composites – Taguchi integrated Supervised Machine Learning Approach