Effects of composition and crystallographic ordering on the ferromagnetic transition in Ni Co Mn In magnetic shape memory alloys
Source abstract
Abstract
Abstract In the present study, the effects of composition and the level of crystallographic ordering were studied in Ni45Co5Mn50-XInX magnetic shape memory alloys with X = 13.55, 13.71 and 13.92, in order to reveal the relationship between the Curie temperature (TC) of the austenite phase and the degree of L21 ordering. Various samples were prepared using secondary heat treatments at different temperatures below the order-disorder transition temperature and for different durations, after solution heat treatment, in order to control the degree of ordering. TCs were determined using calorimetric and magnetometry measurements. The relationship between the TC and the degree of order was used to predict the evolution of the order during selected heat treatments. It was shown that the TC of austenite exhibits a monotonous non-linear dependence on the fraction of the L21 phase. This relationship was corroborated via ab initio numerical calculations within a mean field approximation. Dependence of the crystallographic order parameter on temperature and duration of secondary heat treatments showed that ordering kinetics is fast, the total degree of order depends only on the annealing temperature and cooling rate for long enough treatments, and maximum achievable degree of order is limited by the low thermal energy available for atomic diffusion at low temperatures. Finally, small changes in composition were observed to have a significant impact on martensitic transformation temperatures, and a significant but smaller effect on the ferromagnetic transition in these systems.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Shape Memory Alloy Transformations · Magnetic and transport properties of perovskites and related materials · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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