Magnetic cooling could be a radically different energy solution substituting conventional vapour compression refrigeration in the future. For the largest cooling effects of most potential refrigerants we need to fully exploit the different degrees of freedom such as magnetism and crystal structure. We report now for Heusler-type Ni–Mn–In–(Co) magnetic shape-memory alloys, the adiabatic temperature change ΔT(ad) = −3.6 to −6.2 K under a moderate field of 2 T. Here it is the structural transition that plays the dominant role towards the net cooling effect. A phenomenological model is established that reveals the parameters essential for such a large ΔT(ad). We also demonstrate that obstacles to the application of Heusler alloys, namely the usually large hysteresis and limited operating temperature window, can be overcome by using the multi-response to different external stimuli and/or fine-tuning the lattice parameters, and by stacking a series of alloys with tailored magnetostructural transitions.

Hysteresis is more than just an interesting oddity that occurs in materials with a first-order transition. It is a real obstacle on the path from existing laboratory-scale prototypes of magnetic refrigerators towards commercialization of this potentially disruptive cooling technology. Indeed, the reversibility of the magnetocaloric effect, being essential for magnetic heat pumps, strongly depends on the width of the thermal hysteresis and, therefore, it is necessary to understand the mechanisms causing hysteresis and to find solutions to minimize losses associated with thermal hysteresis in order to maximize the efficiency of magnetic cooling devices. In this work, we discuss the fundamental aspects that can contribute to thermal hysteresis and the strategies that we are developing to at least partially overcome the hysteresis problem in some selected classes of magnetocaloric materials with large application potential. In doing so, we refer to the most relevant classes of magnetic refrigerants La-Fe-Si-, Heusler- and Fe2P-type compounds.This article is part of the themed issue 'Taking the temperature of phase transitions in cool materials'.

The ideal magnetocaloric material would lay at the borderline of a first-order and a second-order phase transition. Hence, it is crucial to unambiguously determine the order of phase transitions for both applied magnetocaloric research as well as the characterization of other phase change materials. Although Ehrenfest provided a conceptually simple definition of the order of a phase transition, the known techniques for its determination based on magnetic measurements either provide erroneous results for specific cases or require extensive data analysis that depends on subjective appreciations of qualitative features of the data. Here we report a quantitative fingerprint of first-order thermomagnetic phase transitions: the exponent n from field dependence of magnetic entropy change presents a maximum of n > 2 only for first-order thermomagnetic phase transitions. This model-independent parameter allows evaluating the order of phase transition without any subjective interpretations, as we show for different types of materials and for the Bean–Rodbell model.

The phase‐down scenario of conventional refrigerants used in gas–vapor compressors and the demand for environmentally friendly and efficient cooling make the search for alternative technologies more important than ever. Magnetic refrigeration utilizing the magnetocaloric effect of magnetic materials could be that alternative. However, there are still several challenges to be overcome before having devices that are competitive with those based on the conventional gas–vapor technology. In this paper a rigorous assessment of the most relevant examples of 14 different magnetocaloric material families is presented and those are compared in terms of their adiabatic temperature and isothermal entropy change under cycling in magnetic‐field changes of 1 and 2 T, criticality aspects, and the amount of heat that they can transfer per cycle. The work is based on magnetic, direct thermometric, and calorimetric measurements made under similar conditions and in the same devices. Such a wide‐ranging study has not been carried out before. This data sets the basis for more advanced modeling and machine learning approaches in the near future.

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