| Autor: |
Akiyasu Yamamoto, Shinnosuke Tokuta, Akimitsu Ishii, Akinori Yamanaka, Yusuke Shimada, Mark D. Ainslie |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
NPG Asia Materials, Vol 16, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1884-4057 |
| DOI: |
10.1038/s41427-024-00549-5 |
| Popis: |
Abstract Iron-based high-temperature (high-T c) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-T c superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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