Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces-Reference-Cited by-同舟云学术

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Published:2023-02-13 Issue:7 Volume:15 Page:10261-10272
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Zhang Zhen¹,Yang Zenan²,Zhao Zhixi¹,Liu Yiyang¹,Wang Chenchong¹,Xu Wei¹^ORCID

Affiliation:

1. State Key Laboratory of Rolling and Automation, School of Materials Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China

2. Science and Technology on Advanced High Temperature Structural Materials Laboratory, Beijing Institute of Aeronautical Materials, Beijing 100095, China

Funder

China Association for Science and Technology

National Science and Technology Major Project

Aero Engine Corporation of China

Financial Stability Support Project

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.2c21439

Reference50 articles.

1. Superhydrophobic TiO 2 /polyvinylidene fluoride composite surface with reversible wettability switching and corrosion resistance

2. Fabrication of ZIF-8@SiO2 Micro/Nano Hierarchical Superhydrophobic Surface on AZ31 Magnesium Alloy with Impressive Corrosion Resistance and Abrasion Resistance

3. Superhydrophobic and photocatalytic synergistic Self-Cleaning ZnS coating

4. Superhydrophobic and Self-Cleaning Bio-Fiber Surfaces via ATRP and Subsequent Postfunctionalization

5. A universal copper mesh with on-demand wettability fabricated by pulsed laser ablation for oil/water separation

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