JOURNAL ARTICLE
Effects of nitrogen flow on the microstructure and mechanical properties of (TiZrNbMoTa)N high-entropy nitride films by magnetron sputtering.
Published In: Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films, 2024, v. 42, n. 4. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Liu, Yuanpeng; Nie, Kaishan; Tian, Shuai; Zhang, Zhengyi; Li, Zheng; Wang, Dong; Hu, Jibo 3 of 3
Abstract
This article focuses on the synthesis and characterization of (TiZrNbMoTa)Nx high-entropy nitride (HEN) films prepared by direct current magnetron sputtering with varying nitrogen flow rates (FN). The study found that increasing FN led to a transition in film microstructure from coarse columnar grains to dense ultrafine grains, a decrease in deposition rate and film thickness, and the formation of a face-centered cubic (FCC) solid solution with binary and ternary nitrides. Mechanical properties such as hardness and elastic modulus peaked at FN = 60 SCCM, reaching 34.39 GPa and 400.97 GPa respectively, due to strong metal-nitrogen covalent bonding, grain refinement, and lattice distortion. Adhesion strength increased with nitrogen flow up to a point but declined at higher FN values due to reduced film uniformity and increased defects. These findings provide insights for designing high-performance high-entropy ceramic coatings suitable for demanding dry-cutting tool applications.
Additional Information
- Source:Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films. 2024/07, Vol. 42, Issue 4, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2024
- ISSN:07342101
- DOI:10.1116/6.0003678
- Accession Number:178228080
- Copyright Statement:Copyright of Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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