journal article·2024-12

Large magnetocaloric effect and negative thermal expansion of Mn-Ni-Si-Fe-Co-Ge high-entropy alloys

P.Y. Huang, Z.G. Zheng, S. Da, Z.G. Qiu, G. Wang, D.C. Zeng

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Abstract

Magnetocaloric high-entropy alloys (HEAs) with a vast compositional space have attracted significant interest for some time due to their great potential in multifunctional application. However, magnetocaloric HEAs generally exhibit modest magnet-entropy change as their undergo second-order phase transition. This work focuses on designing MM`X (M, M′ = transition metals, X = main group elements) HEAs undergoing magnerostructural transition to achieve great magnetocaloric response. For this purpose, Mn xFe 1-xNi 0.6Co 0.4Si 0.62Ge 0.38 ( x = 0.43, 0.45, 0.48, 0.50) with intriguingly crystallographic texture are successfully synthesized and their magnetocaloric effect and negative thermal expansion (NTE) are investigated. The substitution of Fe for Mn reduces the transition temperature ( T t) so that weakens the first-order transition of MM`X HEAs. Besides, Mn 0.55Fe 0.45Ni 0.6Co 0.4Si 0.62Ge 0.38 undergoing first-order transition from Ni 2In-paramagnetic-austenitic phase to TiNiSi-ferromagnetic-martensitic phase exhibits a great magnetic entropy change of 16 J∙kg −1K −1 at 206 K for 2 T. Moreover, an obviously NTE is observed through phase transition with liner NTE coefficient of −6.77×10 −6 K −1. In addition, Mn 0.55Fe 0.5Ni 0.6Co 0.4Si 0.62Ge 0.38 displays an intriguing NTE with liner NTE coefficient of −1401.7×10 −6 K −1 (220–270 K) along the direction parallel to field in a temperature range from 220 K to 270 K, which is much higher than typical NTE materials. Therefore, the present work demonstrates that MM`X HEAs exhibit potential to achieve some magnetocaloric and NTE multifunctional application.
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Research context

OpenAlex topics: Thermal Expansion and Ionic Conductivity · Magnetic and transport properties of perovskites and related materials · Shape Memory Alloy Transformations

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

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