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Machine Learning-Enhanced Fabrication of Three-Dimensional Co-Pt Microstructures via Localized Electrochemical Deposition

Authors :
Yangqianhui Zhang
Zhanyun Zhu
Huayong Yang
Dong Han
Source :
Mathematics, Vol 12, Iss 21, p 3443 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face significant challenges in controlling the thickness and uniformity of deposition layers, particularly in the manufacturing of magnetic materials. To address these challenges, this paper proposes a method that integrates machine learning algorithms to optimize the electrochemical deposition parameters, achieving a Co:Pt atomic ratio of 50:50. This optimized ratio is crucial for enhancing the material’s magnetic properties. The Co-Pt microstructures fabricated exhibit high coercivity and remanence magnetization comparable to those of bulk Co-Pt magnets. Our machine learning framework provides a robust approach for optimizing complex material synthesis processes, enhancing control over deposition conditions, and achieving superior material properties. This method opens up new possibilities for the fabrication of 3D microstructures with complex shapes and structures, which could be useful in a variety of applications, including micro-electromechanical systems (MEMSs), micro-robots, and data storage devices.

Details

Language :
English
ISSN :
22277390
Volume :
12
Issue :
21
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.941074ac2e224e7fa8d97db0df62a171
Document Type :
article
Full Text :
https://doi.org/10.3390/math12213443