journal article·2024-12

Improved elastocaloric effect of NiTi shape memory alloys strengthened by Ni4Ti3 nanoprecipitates

Qixing Zhang, Yudong Wang, Xu Xiao, Juxin Zhang, Wenjie Zhang, Meiling Liu, Beihai Huang, Bo Xu, Chao Yu, Chong Wang, Qingyuan Wang

Acta Materialia· Volume 281
Publisher page

Abstract

NiTi shape memory alloys (SMAs) exhibit extraordinary potential in the field of solid-state cooling due to their exceptional elastocaloric effect (eCE). However, the inherent trade-off between cooling efficiency and cooling capacity often makes it difficult to enhance both simultaneously, and the relatively poor cyclic stability poses a significant challenge to the application of such material as a solid-state refrigerant. A NiTi elastocaloric material containing uniformly distributed high-density Ni 4Ti 3 nanoprecipitates is obtained in this work, with a remarkable coefficient of performance of material ( COP mat) as high as 52 and a satisfactory adiabatic temperature change (Δ T ad) of 11 K, breaking through the limits of the eCE reported in existing SMAs. The precipitate-strengthened NiTi SMA fabricated in this work exhibits a low hysteresis and excellent cyclic stability, with the former attribute stemming from the strain-glass-like phase transformation and its reverse induced by the high-density Ni 4Ti 3 nanoprecipitates, and the latter resulting from the effective suppression of dislocation slip by the uniformly distributed high-density Ni 4Ti 3 nanoprecipitates. The precipitate-strengthened NiTi SMAs with a <001>{111} texture exhibit great eCE and simultaneously possess good strength and superelasticity controllability, which can maintain excellent cyclic stability even at a high stress level of 800 MPa. The precipitate-strengthened NiTi SMAs developed in this work not only hold significant advantages for solid-state refrigeration applications but also demonstrate extraordinary potential in high-precision control devices.
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Research context

OpenAlex topics: Shape Memory Alloy Transformations · Creativity in Education and Neuroscience

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

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