journal article·2025-01

Atomic-scale revelation of Cu diffusion mechanism in high-performance Pr-doped 2:17-type SmCo magnets

Chaoqun Zhu, Zhuang Liu, Hubin Luo, Haichen Wu, Guoxin Chen, Yang Yang, Yu Liu, Dongliang Tan, Yaowen Li, Rui Xia, Renjie Chen, Aru Yan

Acta Materialia· Volume 282
Publisher page

Abstract

The coercivity of 2:17-type SmCo magnets is mainly determined by the gradient distribution of Cu within the cell boundary phase. However, the fundamental migratory behavior and driving force causing such Cu distribution remains unclear. In this work, the phenomenon of Cu enrichment at the lamellar/intracellular interface is reported at the atomic scale, and a new Cu diffusion mechanism of 2:17-type SmCo magnets is proposed via combining microstructure characterization with first-principles calculation. Our study indicates that Cu will migrate from the intracellular phase to the lamellar/intracellular phase interface following the migration of twin boundary, and then diffuses further along the lamellar phase interface to the cell boundary, achieving the expected Cu-rich cell boundary structure. Doping Pr results in a reduction of Cu substitution energy in the lamellar phase, leading to a decreased driving force during the subsequent Cu diffusion. As a result, the Cu does not completely diffuse into the cell boundary phase there is Cu enrichment at the interface between lamellar and intracellular phases. The Cu concentration of the cell boundaries could be increased in 2:17-type SmCo magnets by reasonable designing the substitution energy difference of intracellular phase, lamellar phase and cell boundary phase that promote Cu diffusion to cell boundary. Our study sheds light on the control of Cu segregation in magnets and a new strategy for enhancing the magnetic performance of 2:17-type SmCo magnets.
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Research context

OpenAlex topics: Magnetic Properties of Alloys · Magnetic and transport properties of perovskites and related materials · Rare-earth and actinide compounds

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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