Research and Application of High Entropy Alloys

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Plasma Coatings, Surfaces & Interfaces".

Deadline for manuscript submissions: 29 November 2024 | Viewed by 1244

Special Issue Editor


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Guest Editor
Innovation Base, Josai International University, Togane 283-8555, Japan
Interests: high-entropy metallic glass; glassy alloys

Special Issue Information

Dear Colleagues,

High-entropy alloys (HEAs) are a new class of metallic materials that contain multiple principal elements in roughly equal atomic proportions, resulting in high configurational entropy and unique microstructures. HEAs have attracted significant attention in recent years due to their potential applications in various fields. This Special Issue invites researchers to contribute their latest findings and progress in the field of HEA research and application. Topics of interest include, but are not limited to the following:

  • Processing and fabrication, and novel alloy design strategies with targeted properties;
  • Mechanical properties and deformation mechanisms;
  • Wear and corrosion resistance;
  • Oxidation resistance and high-temperature stability;
  • Magnetic, electrical, and catalytic properties, and biocompatibility;
  • Computational modeling and simulations;
  • Applications and future directions.

Dr. Fanli Kong
Guest Editor

Manuscript Submission Information

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Keywords

  • high-entropy alloys
  • mechanical properties
  • corrosion resistance
  • applications
  • microstructure

Published Papers (3 papers)

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Research

11 pages, 4100 KiB  
Article
The Effects on Stability and Electronic Structure of Si-Segregated θ′/Al Interface Systems in Al-Cu Alloys
by Lu Jiang, Zhihao Zhao and Gaosong Wang
Coatings 2024, 14(7), 879; https://doi.org/10.3390/coatings14070879 - 13 Jul 2024
Viewed by 348
Abstract
This study systematically investigates the energy and electronic properties of Si-segregated θ′(Al2Cu)/Al semi-coherent and coherent interface systems in Al-Cu alloys using ab initio calculations. By evaluating the bonding strength at the interface, it has been revealed that Si segregated at the [...] Read more.
This study systematically investigates the energy and electronic properties of Si-segregated θ′(Al2Cu)/Al semi-coherent and coherent interface systems in Al-Cu alloys using ab initio calculations. By evaluating the bonding strength at the interface, it has been revealed that Si segregated at the A1 site (Al slab) of the semi-coherent interface systems exhibits the most negative segregation energy, resulting in a noticeable decrease in total energy and an increase in interface adhesion. The electronic structure analysis indicates the presence of Al-Cu and Al-Al bonds, with Si occupying the A1 site. The strong bond formation between Al-Cu and Al-Al is essential for improving interface bonding strength. The results of the calculating analyses are consistent with the results of the previous experiments, and Si can be used as a synergistic element to reduce the θ′/Al interface energy and further reduce the coarsening drive of the θ′ precipitated phase, which can provide new perspectives and computational ideas for the compositional design of heat-resistant Al-Cu alloys. Full article
(This article belongs to the Special Issue Research and Application of High Entropy Alloys)
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11 pages, 7890 KiB  
Article
Effect of Cooling Method on Microstructure and Microhardness of CuCrFeMnNi High-Entropy Alloy
by Yajun Zhou, Ruifeng Zhao, Hechuan Geng, Bo Ren, Zhongxia Liu, Jianxiu Liu, Aiyun Jiang and Baofeng Zhang
Coatings 2024, 14(7), 831; https://doi.org/10.3390/coatings14070831 - 3 Jul 2024
Viewed by 343
Abstract
This study investigated four cooling methods for CuCrFeMnNi high-entropy alloy, namely, furnace cooling, air cooling, oil cooling, and water cooling (designated as FC, AC, OC, and WC, respectively), following a 12 h treatment at 800 °C. Results indicate that all four cooled alloys [...] Read more.
This study investigated four cooling methods for CuCrFeMnNi high-entropy alloy, namely, furnace cooling, air cooling, oil cooling, and water cooling (designated as FC, AC, OC, and WC, respectively), following a 12 h treatment at 800 °C. Results indicate that all four cooled alloys consisted of two FCC solid-solution phases (FCC1 and FCC2) and ρ phases. However, the FC alloy primarily contained FCC2 as the main phase and FCC1 as the secondary phase. The other three cooling methods yielded alloys with FCC2 as the primary phase and FCC1 as the secondary phase. With an increase in cooling rate, the content of the FCC1 phase gradually increased, that of the ρ phase initially decreased and then increased, and that of the FCC2 phase gradually decreased. The microstructure of the CuCrFeMnNi high-entropy alloy under the four cooling methods consisted of gray-black dendrites rich in Cr-Fe and white dendrites rich in Cu. Black ρ-phase particles predominated the dendrite region. As the cooling rate increased, the white interdendritic regions shrank, and the gray-black interdendritic regions expanded. The FC alloy exhibited the lowest microhardness at approximately 202.6 HV. As the cooling rate increased, the microhardness of the alloy progressively increased. The microhardness of the WC alloy was the highest, at approximately 355 HV. The strengthening mechanisms for all the alloys were primarily solid-solution strengthening and second-phase precipitation strengthening. Full article
(This article belongs to the Special Issue Research and Application of High Entropy Alloys)
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9 pages, 3098 KiB  
Communication
Predicting New Single/Multiphase-Structure High-Entropy Alloys Using a Pattern Recognition Network
by Fang Wang, Jiahao Wang, Jiayu Wang, Ruirui Wu and Ke Liu
Coatings 2024, 14(6), 690; https://doi.org/10.3390/coatings14060690 - 1 Jun 2024
Viewed by 325
Abstract
Machine learning methods were employed to predict the phase structures of high-entropy alloys (HEAs). These alloys were classified into four categories: bcc (body-centered cubic), fcc (face-centered cubic), bcc+fcc (body-centered cubic and face-centered cubic) and others (containing intermetallic compounds and other structural alloys). The [...] Read more.
Machine learning methods were employed to predict the phase structures of high-entropy alloys (HEAs). These alloys were classified into four categories: bcc (body-centered cubic), fcc (face-centered cubic), bcc+fcc (body-centered cubic and face-centered cubic) and others (containing intermetallic compounds and other structural alloys). The utilized algorithm was a Pattern Recognition Network (PRN) utilizing cross-entropy as the loss function, enabling the prediction of HEAs’ phase formation probability. The PRN algorithm demonstrated an accuracy exceeding 87% based on the test data. The PRN algorithm successfully predicted the transformation from fcc to fcc+bcc and subsequently to a bcc structure with the increase in Al content in AlxCoCu6Ni6Fe6 and AlxCoCrCuNiFe HEAs. In addition, AlxCoCu6Ni6Fe6 (x = 1, 3, 6, 9) HEAs were prepared using a vacuum arc furnace, and the microstructure of the as-cast alloy was tested by means of XRD, SEM, and EBSD, confirming the high consistency between the predicted and observed phase structures. This study showcases the efficacy of the PRN algorithm in predicting both single- and multiphase-structure high-entropy alloys, offering valuable insights into alloy design and development. Full article
(This article belongs to the Special Issue Research and Application of High Entropy Alloys)
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