Magnetocaloric properties of metallic nanostructures

Abstract

A compilation of magnetocaloric properties of metallic nanostructures with Curie temperature (T_C) between 260 and 340 K has been tabulated. The tabulated data show that nanostructure plays an important role in enhancing the magnetocaloric properties of a material, namely by reducing the peak of magnetic entropy, but broadening of the magnetocaloric effect curve with an average of 10 K sliding window for Curie temperature. A second table lists all bulk metallic and intermetallic materials, in which there is no nanostructural data, with an entropy change of at least 20 J/kg K and a Curie temperature between 260 and 340 K. We propose that further experiments should be made on the nanostructured form of these materials.

Keywords:

• nanostructure
• magnetocaloric effect
• magnetic refrigeration
• Curie temperature
• entropy change
• relative cooling power

Public Interest Statement

In contrast with conventional refrigerator systems that work based on compression and evaporation of gas, magnetic refrigeration systems work based on magnetizing and demagnetizing a magnetic material. Magnetic refrigeration is an energy-efficient and environmentally friendly refrigeration technology with the following desirable characteristics: (1) it does not use ozone-depleting and hazardous chemicals or greenhouse gases; (2) the cooling efficiency of magnetic refrigeration is significantly higher than conventional cooling technology, compressor-based techniques; and (3) the magnetic refrigerator can be built more compactly and generates less noise. Magnetic refrigeration exploits a property of magnetic materials called the magnetocaloric effect (MCE). A through literature survey has been done in this article and it has been concluded that metallic nanostructures are the best materials to be used in a magnetic refrigeration system compared to their bulk counterparts. Moreover, this paper suggests some new materials to be synthesized as nanostructures and their MCE properties studied.

1. Introduction

Magnetic refrigeration technology, as a promising alternative to conventional gas compression cooling, has attracted extensive attention due to its good energy efficiency, and environmentally friendly nature. Materials with Curie temperature near room temperature with good magnetocaloric effect (MCE) properties and negligible hysteresis are of special interest.

Particle size, shape, morphology, chemical composition, structure, and interaction of the particle with the surrounding matrix and neighboring particles all have a profound effect on the magnetic behavior of the material. Any material with discrete functional parts, with any internal, external, or surface structure dimension of the order of 100 nm or less is classified as nanostructured material. Nanostructures can be synthesized in different shapes such as nanowires, nanotubes, etc. Differentiation between the numbers of dimensions on the nanoscale describes a nanostructured material. It has been shown that nanostructures are the best choice for magnetic refrigeration systems (Bennett et al., 1995; Della Torre, Bennett, & Jin, 2012; Shir, Yanik, Bennett, Della Torre, & Shull, 2003; Shull, Swartzendruber, & Bennett, 1991). Nanostructures have a higher MCE over a temperature distribution so they exhibit more cooling efficiency as compared to bulk materials. The entropy change ΔS, determines the cooling efficiency and it is largest at lower fields. The magnitude of entropy change varies for different materials based on the number of particles per unit volume, magnetic moment of the particles, and the order parameter (Shir et al., 2003). An example of this can be seen in Figures 1 and 2 for gadolinium. Magnetocaloric properties of a nanostructure can be tuned and optimized by controlling its properties such as size, shape, morphology, etc. Therefore, the MCE peak can be displaced to other temperatures, broadened, or made sharper. This point has been amplified, by gathering all nanostructures with Curie temperature in the range of 260–340 K in Table 1 and comparing their MCE properties to their bulk counterparts for materials available in the literature. Furthermore, all bulk materials reported in the literature with magnetic entropy of at least 20 J/kg K and a Curie temperature between 260 and 340 K have been arranged in Table 2. This list is of importance for future research since there are no data available for the nanostructurized form of these bulk materials.

Figure 1. Calculated change in entropy, |∆S_m|, of nanocomposite clusters of Gd and Gd alloys between 0 and 2T vs. temperature.

Notes: The one atom calculation agrees with the bulk Gd experimental results. The alloys must be chosen so as to keep the ordering temperature the same as that of bulk Gd (Shir et al., 2003).

Figure 2. MCE (∆T) vs. temperature for the operating lines: ideal Gd bulk material and a calculated Gd nanocomposite (Shir et al., 2003).

Table 1. Studied nanostructured materials and their bulk counterparts

Sample	Size (nm)	TC (K)	Δ|SM| (J/kg K)	ΔTad (K)	ΔH (kOe)	RCP (J/kg)	Reference
Pr2Fe17	20	292	4.5		50	573	Gorria et al. (2008)
	B	286	6.4		50	506	Gorria et al. (2008)
	20	292	2.1		15	107	Alvarez, Sánchez-Marcos, et al. (2010)
	B	285	2.6		15	105	Alvarez, Sánchez-Marcos, et al. (2010)
Nd2Fe17	14	340	1.6		15	83	Alvarez, Gorria, et al. (2010)
	24	340	1.85		15	87	Alvarez, Gorria, et al. (2010)
	B	339	2.6		15	87	Alvarez, Gorria, et al. (2010)
	17	340	1.2		15	87	Alvarez, Sánchez-Marcos, et al. (2010)
	B	339	1.7		15	82	Alvarez, Sánchez-Marcos, et al. (2010)
NdPrFe17	20	303	2.1		20	175	Sánchez-Valdés et al. (2014)
Fe72Ni28		333	0.5		50	250	Ucar et al. (2013)
Fe70Ni30		333	0.32		50	342	Ucar et al. (2013)
(Fe70Ni30)97Mo3		320	1.69		50	440	Ucar et al. (2014)
(Fe70Ni30)96Mo3		300	1.67		50	432	Ucar et al. (2014)
Co:Ni67Cu33	2	260	0.15		10	18^*	Michalski et al. (2012)
		270	0.72		70	108^*	Michalski et al. (2012)
Co/Cr superlattices		334	0.27^*	0.3	50		Mukherjee et al. (2009)
Fex Pt100−x							Rong et al. (2007)
CuCr2Te4	22	330					Ramasamy et al. (2012)
MnAs0.97P0.03	23	275	14		50	294^*	Sun et al. (2012)
Gd	15	288		1.1			Zeng, Kuang, Zhang, and Yue (2012)
	100	296		2.1			Zeng et al. (2012)

Notes: Size (nm), Curie temperature (K), magnetic entropy change (J/kg K), adiabatic temperature (K), magnetic field (kOe), and RCP_FWHM (J/kg). B refers to bulk.

^* Own calculation from the figures.

Table 2. Metallic and intermetallic bulk materials with magnetic entropy of at least 20 J/kg K

Sample	TC (K)	Δ|SM| (J/kg K)	ΔTad (K)	ΔH (kOe)	RCP (J/kg)	Reference
LaFe11.2 Co0.7 Si1.1	274	20.3		50	670^*	Hu, Shen, Sun, Wang, and Cheng (2002)
La(Fe0.94Co0.06)11.9Si1.1	274	20^**	2.5	50	400^*	Hu et al. (2005)
LaFe11.7 Si1.3Co0.2H1.7	278	23.9		50	956^*	Zhang et al. (2012, 2013)
La(Fe0.89 Si0.11)13 H1.4	298	24^**		20	192^*	Fujieda, Fujita, Fukamichi, and Suzuki (2011)
La0.5 Pr0.5 Fe11.4 Si1.6 H0.9	275	24.1		50	366	Zhao et al. (2010)
La0.5 Pr0.5 Fe11.4 Si1.6 H1.6	317	22.1		50	351	Zhao et al. (2010)
La(Fe0.88Si0.12)13H1.5	325^**	24	12.6	50		Fujieda, Fujita, and Fukamichi (2007)
La0.5Pr0.5(Fe0.88Si0.12)13H1.6	320^**	26	13.6	50		Fujieda et al. (2007)
La(Fe0.88Si0.12)13H	274	23	11.1	50	460^*	Fujita, Fujieda, Hasegawa, and Fukamichi (2003)
La(Fe0.88Si0.12)13H1.5	323	23	12.6	50	506^*	Fujieda et al. (2003)
La(Fe0.89Si0.11)13H1.3	291	28	12.8	50	–	Fujieda et al. (2003)
La(Fe0.9Si0.1)13H1.1	287	31	15.4	50	–	Fujieda et al. (2003)
Gd5Si1.95−xGe2.04Sn0.01	270	22^**		20	132^*	Li et al. (2006)
Gd5Si1.95−xGe2.02Sn0.03	277	28.9		20	87^*	Li et al. (2006)
Gd5Si1.95−xGe2Sn0.05	268	25^**		20	188^*	Li et al. (2006)
Gd5Si1.95−xGe1.95Sn0.1	260	23^**		20	69^*	Li et al. (2006)
Mn0.9Cr0.1 As	267	20.2		50	283^*	Sun et al. (2008)
MnAs	318^**	38^**		50	418^*	Balli et al. (2008)
Mn0.9Ti0.05 V 0.05 As	266^**	31^**		50	256^*	Balli et al. (2008)
MnAs0.927Sb0.073	305^**	27.5^**		10	55^*	Kim, Wada, and Itoh (2007)
MnAs	316	28.8–40		20–50	267	Tocado, Palacios, and Burriel (2006, 2009), Gama et al. (2004), Mosca, Vidal, and Etgens (2008)
Mn0.99Fe0.01As	294	26		20		Balli, Fruchart, Gignoux, and Zach (2009)
Mn0.9875Cr0.0125As	300^**	38	15^**	50	893	Mejía, Gomes, Reis, and Rocco (2011)
Mn0.9875Cr0.0065Fex0.006As	287^**	42	13.5^**	50	730	Mejía et al. (2011)
Mn0.94Fe0.06As	289	29.3		50		Lima Sharma, Gama, Coelho, and de Campos (2008)
Mn0.94Cu0.06As	308	21		50		Lima Sharma et al. (2008)
MnAs0.93Sb0.07	273^**	30^**		29	260^*	Wada, Matsuo, and Mitsuda (2009)
MnAs0.99P0.01	301^**	40	13	140	–	Mitsiuk, Govor, and Budzynski (2013)
MnAs0.98P0.02	296^**	50	15	140	400^*	Mitsiuk et al. (2013)
Mn0.985Fe0.015As	280^**	30^**		80	300^*	Bratko et al. (2012)
MnFeP0.5 As078Ge0.22	280	38^**		50	760^*	Dagula et al. (2005)
MnFeP0.5 As077Ge0.23	309^**	31^**		50	465^*	Dagula et al. (2005)
MnFeP0.5As0.4Si0.1	303	20		30	240^*	Dagula et al. (2006)
MnFeP0.5As0.3Si0.2	294	32		30	192^*	Dagula et al. (2006)
MnFeP0.5As0.2Si0.3	280	21		30	210^*	Dagula et al. (2006)
Mn1.1Fe0.9P0.76Ge0.24 (Ribbon)	317	35.4		50	496^*	Yan, Müller, Schultz, and Gutfleisch (2006)
Mn1.1Fe0.9P0.76Ge0.24 (Bulk)	299	26.1		50	522^*	Yan et al. (2006)
MnFe0.9P0.78Ge0.22	296	20		20	200^*	Trung et al. (2009)
Mn1.2Fe0.8P0.75Ge0.25	288	20.3		20	151	Trung et al. (2009)
Mn1.1Fe0.9P0.5As0.5	274^**	21		40	300	Gribanov et al. (2009)
Mn1.1Fe0.9P0.8Ge0.2	267.5	44.35		30	311^*	Yue et al. (2010)
Mnx1.20Fe0.75P0.50Si0.50	299^**	26^**		20	260^*	Dung, Zhang, Ou, Brück, and Zhang (2011)
Mnx1.25Fe0.7P0.50Si0.50	285^**	22^**		20	176^*	Dung et al. (2011)
Mn1.1Fe0.9P0.76Ge0.24	280^**	46.5		50	558^*	Yue et al. (2013)
Mn1.1Fe0.9P0.78Ge0.22	298	20	4	20	160^*	Sougrati et al. (2008)
Mn0.92Cu0.08CoGe	321	53.3		50	267^*	Samanta, Dubenko, Quetz, Stadler, and Ali (2012)
Mn0.915Cu0.085CoGe	304	52.5		50	210^*	Samanta et al. (2012)
Mn0.91Cu0.09CoGe	289	41.2		50	124^*	Samanta et al. (2012)
Mn0.905Cu0.095CoGe	275	34.8		50	314^*	Samanta et al. (2012)
MnCo0.55Zn0.45Ge	327	26		50	286^*	Samanta, Dubenko, Quetz, Stadler, and Ali (2013)
MnCo0.95Zn0.05Ge	281	31.4		50	252^*	Samanta et al. (2013)
Mn0.93Cr0.07CoGe	265^**	23^**		50	230^*	Caron, Trung, and Brück (2011)
Mn2.6Fe0.4Sn2	260	24.6		50	1,380	Recour, Mazet, and Malaman (2008)
Mn2.5Fe0.5Sn2	261	23.9		50	1,390	Recour et al. (2008)
Mn2.4Fe0.6Sn2	268	25.3		50	1,570	Recour et al. (2008)
Mn2.3Fe0.7Sn2	271	24.4		50	1,510	Recour et al. (2008)
Mn2.2Fe0.8Sn2	283	23		50	1,380	Recour et al. (2008)
Mn2.1Fe0.9Sn2	290	20.6		50	1,350	Recour et al. (2008)
Gd7Pd3	328^**	56.8	8.5	50	373	Canepa, Cirafici, Napoletano, and Merlo (2002)
Gd7Pd3Ni	315^**	41.1	6.8	50	240	Canepa et al. (2002)
Gd76Pd24	300^**	56.1	8.4	50	422	Canepa et al. (2002)
Fe80Pt20	283^**	25.2		50	530^*	Rongm and Ping Liu (2007)

Notes: Curie temperature (K), magnetic entropy change (J/kg K), adiabatic temperature (K), magnetic field (kOe), and RCP (J/kg).

^* Own calculation from the figures.

^** Approximation from the figures in the references.

Though much research has been done on employing oxide nanostructures, oxide nanostructures have less magnetization and will not be able to compete with metallic nanostructures. Therefore, metallic and intermetallic nanostructures seem to be the best choice for magnetic refrigeration systems.

2. Methodology

Nanostructures have several advantages as a refrigerant for 100–300-K applications compared to the other common methods of assembling a magnetic refrigeration bed, such as a layered thermal bed (Shir et al., 2003) or mixing of different magnetic materials. In order to predict and evaluate the MCE of a nanostructure, refrigerant capacity can be considered as the basis for calculating the temperature behavior of the material. The relative cooling power (RCP) is defined as(1) $$\text{RCP}=-\mathrm{\Delta }{S}_M(T,H)\times \mathit{\delta }{T}_{\text{FWHM}},$$(1)

where ∆S_M (T, H) is the refrigerant’s magnetic entropy change as a function of temperature and magnetic field and δT_FWHM is the full width at half maximum (FWHM) of the peak of magnetic entropy. This parameter measures how much heat can be transferred between the cold and hot heat exchangers in an ideal refrigeration cycle (Gschneidner, Pecharsky, Pecharsky, & Zimm, 1999; McMichael, Shull, Swartzendruber, Bennett, & Watson, 1992). Increasing the RCP not only increases the amount of refrigeration obtainable from the particular refrigerant and field excursion, but also tends to increase the thermodynamic efficiency of the cycle, thus measuring how well a particular volume or quantity of refrigerant is utilized. Improvement in RCP mainly relies on broadening the magnetic entropy change by either coupling two phases of magnetic materials with desirable properties or nanostructure synthesis with the main motivation rooted in their inherent tendency to have distributed exchange coupling, which will broaden the magnetic entropy curve (Ucar, Ipus, France, Mchenry, & Laughlin, 2012).

The sum rule (McMichael et al., 1992) implies that for materials with the same saturation magnetic moments, M_o, those with high entropy change at a given temperature will have low entropy changes at other temperatures, and materials which do not have a large entropy change at any particular temperature can undergo a moderate entropy change over a broader temperature range. Magnetization of suitable nanostructures can be switched by an applied magnetic field of the order 1 T (10 kOe) or less, which is easily realized without using superconducting magnet (Skomski, Binek, Mukherjee, Sahoo, & Sellmyer, 2008).

The need for obtaining materials with a desired entropy change leads to the need for accurate modeling and simulation of entropy change for materials. Work by Bennett, McMichael, Tang, and Watson (1994) and Bennett, McMichael, Swartzendruber, and Shull (1992) shows how Monte Carlo simulations can be used to predict entropy change for a nanostructure with ferromagnetically interacting clusters. Results in McMichael et al. (1992) and Bennett et al. (1992) show that magnetic nanostructures can possess enhanced magnetocaloric properties at high temperatures and low fields. Materials that rely on nanostructures include paramagnetic salts used for attaining low temperatures and superparamagnetic particles for intermediate temperatures, while on ferromagnetic to paramagnetic or magnetostructural phase transformation at room temperature (Ucar et al., 2012).

Work by Shir et al. (2003) emphasizes that magnetic nanostructures with interacting clusters are a means by which to readjust magnetic entropy (∆S) to make it more uniform with the temperature and to provide enhanced magnetic entropy (∆S) values at temperatures above their effective interaction temperature. In addition, they revealed that by appropriate selection of nanostructure materials, MCE vs. T curve can be fine-tuned to a specific curve as shown in Figures 1 and 2. Magnetic entropy (ΔS) constancy can be fulfilled and in the reversible case, the cycle will have an efficiency equivalent to the Carnot cycle.

Work by Shull (1993) shows experimental and theoretical verifications of the enhancement of magnetocaloric properties of a superparamagnetic nanostructure material Gd₃Ga_5−xFe_xO₁₂ (GGIG) at high temperatures and low fields. In addition, the MCEs were found to be 3–4 times larger than a low temperature paramagnetic refrigerant (GGG).

Nanostructuring improves the entropy change in low fields and high temperatures, but yields some reduction in the maximum entropy change per atom. An important feature of magnetic nanoparticles is that the entropy exhibits a strong dependence on the magnetic anisotropy which can be exploited for magnetic cooling (Skomski et al., 2008). In principle, anisotropy of nanostructures can be tailored almost at will, being possible to control not only its magnitude but also the distribution of easy axes orientation, leading to MCE response that is qualitatively different to that of the bulk material (Franco et al., 2008). Inter-particle and intra-particle interactions modify the magnetic responses for an arrangement of nanoparticles such as anisotropy energy (Skomski et al., 2008), Zeeman energy, and dipolar interaction energy. The total energy is given by(2) $$E=\sum _i{E}_A^{(i)}+\sum _i{E}_H^{(i)}+\frac{1}{2}\sum _i\sum _{j\ne i}{E}_D^{(i,j)},$$(2)

in which, the anisotropy and Zeeman energy terms are $E_A^{(i)}=-k{V}_i{\left(({\mathit{\mu }}_i·n_i)/\left|{\mathit{\mu }}_i\right|)\right)}^2$ and $E_H^{(i)}=-{\mathit{\mu }}_i·H$, respectively. The magnetic dipole energy between two particles i and j, separated by a distance r_ij, with magnetic moments μ_i and μ_j, and the volume corresponding to the ith particle V_i, is $E_D^{(i,j)}=\frac{{\mathit{\mu }}_i·{\mathit{\mu }}_j}{r_{ij}^3}-\frac{3({\mathit{\mu }}_i·r_{ij})({\mathit{\mu }}_i·r_{ij})}{r_{ij}^5}$.

Inter-particle interactions’ strength exhibits three magnetic stages: (1) Superparamagnetism (SPM) for no interaction, (2) Superspin glass (SSG) for intermediate interaction strengths, and (3) Superferromagnetism for highly interacting superspins (Bedanta & Kleemann, 2009). Detailed studies on the effect of different parameters e.g. particle size, inter-particle distance, magnetic field, and temperature on the above states are discussed in Chen, Sahoo, Kleemann, Cardoso, and Freitas (2004) and Petracic et al. (2006).

3. Sample preparation

Properties of materials, in general, and nanoparticles, in particular, are influenced by shape, size, and other parameters controlled by sample preparation. Hence, a special interest in the field of sample preparation. Broadly techniques can be classified into top-down or bottom-up approaches as shown in Figure 3.

Figure 3. Nanostructure fabrication processes.

Many current strategies combine both synthesis and assembly into a single process (Gubin, Koksharov, Khomutov, & Yurkov, 2005; Saravanan, Gopalan, & Chandrasekaran, 2008). Some of these techniques are

•	High-temperature Thermal Decomposition which is suitable for preparing films of nanoparticles (Saravanan et al., 2008).
•	Gas–liquid Interface for the synthesis of metallic nanoparticles in the absence of solid substrates or matrices by oxidation–reduction (redox) reactions at an interface between two phases, one containing a metal compound and the other, the reducing agent (Saravanan et al., 2008).
•	Electrolytic erosion and electrochemical generation are examples of Mechanochemical dispersion, which is an attractive way to produce disperse systems with the size of the particles depending upon the density of the electric current (Saravanan et al., 2008).
•	Liquid–liquid Interface Reaction not only synthesizes nanoparticles but also casts them into a film in one process (Gubin et al., 2005).
•	Vaporization (laser, thermal, plasma, arch discharge, and solar energy-induced) of supersaturated metal allows to study physiochemical characteristics and properties at the gas and solidified phases (Gubin et al., 2005).
•	Thermolysis or Thermal decomposition of metal-containing compounds is carried out in a liquid medium in the presence of surfactants or polymers resulting in nanoparticles with diameters up to 10 nm (Gubin et al., 2005).
•	Decomposition of metal-containing compounds by ultrasonic treatment is carried out in metal carbonyls and their derivatives being used as metal-containing compounds (Gubin et al., 2005).
•	Using strong reducing agents e.g. alkali metal dispersions in ethers or hydrocarbons, alkali metal complexes with organic electro acceptors or high-boiling alcohols, and magnetic metallic nanoparticles can be prepared by the Reduction method (Gubin et al., 2005).
•	Sol–gel method, though mostly used to obtain metal oxides, is being used for the synthesis of nanosized metals (Gubin et al., 2005).
•	High-energy ball milling is a widely used technique for synthesizing new metastable alloys out from the thermodynamic equilibrium such as amorphous metallic glasses or disordered and supersaturated solid solutions with achieving a size of below 10 nm (Michalski et al., 2012; Ucar, Ipus, Laughlin, & McHenry, 2013).

Due to sensitivity of nanostructures to milling intensity, temperature and other factors have far-reaching effects on their properties (Saravanan et al., 2008).

4. Metallic nanostructure survey

Table 1 demonstrates that nanostructures are a better choice compared to their bulk counterpart to be utilized in the magnetic refrigeration system. Even though the bulk material has a higher magnetic entropy change, the RCP of the nanostructure is higher than the bulk counterpart. This is very important in the magnetic refrigeration system since increasing the RCP not only increases the amount of refrigeration obtainable from the particular refrigerant and field excursion, but also tends to increase the thermodynamic efficiency of the cycle.

The reduction in magnetization for bulk Pr₂Fe₁₇ goes down sharply with temperatures above 280 K, while for the nanostructure Pr₂Fe₁₇, it decreases slowly. At 150 K, the magnetization is 50% greater in the bulk sample but under higher magnetic field, the situation is different, as seen in Figure 4 (Gorria et al., 2008).

Figure 4. Temperature dependence of the magnetization for both bulk and BM-10h Pr₂Fe₁₇ alloys under an applied magnetic field of μ₀H = 100 mT.

Note: The inset shows the magnetization vs. applied curves at T = 4 K (Gorria et al., 2008).

Figure 5 shows the improvement in the RCP of a nanostructure compared to its bulk counterpart. Even though the magnetic entropy is higher in the bulk material, the nanostructure Pr₂Fe₁₇ has 60% higher δT_FWHM compared to the bulk (Gorria et al., 2008).

Figure 5. Temperature dependence of calculated Δ|S_M| and RCP values under an applied magnetic field, μ₀H = 5 T, for pure Gd, bulk Pr₂Fe₁₇, and BM-10h Pr₂Fe₁₇.

Note: The RCP values were estimated in two different ways and are shown as the shaded areas and striped rectangles for each material (Gorria et al., 2008).

Three samples of Nd₂Fe₁₇ at different particle sizes practically show same MCE properties, but δT_FWHM becomes broader with the increase in the milling time and hence reduction in the size. For nanostructure Nd₂Fe₁₇ sample, δT_FWHM is 72, while for the bulk it is 50 (Alvarez, Gorria, et al., 2010; Alvarez, Sánchez-Marcos, et al., 2010) as shown in Figure 6. A slight improvement of about 5% (Alvarez, Gorria, et al., 2010) is observed in RCP, but in the case of Pr₂Fe₁₇, as seen in Table 1, the improvement in MCE is not that great in the applied magnetic field of 15 kOe.

Figure 6. Temperature dependence of Δ|S_M| under H = 15 kOe for bulk and milled Nd₂Fe₁₇ samples.

Notes: The horizontal lines represent δT_FWHM for each sample. The inset shows the magnetic field dependence of δT_FWHM for all the samples (Alvarez et al., 2010).

Curie temperature of Ni nanostructure decreases with the reduction in grain size with a dramatic drop below 10 nm, but its Curie temperature is still above room temperature. The Fe–Ni system, on the other hand, is a good economical and efficient alternative to rare earth-based alternatives. Fe–Ni-based alloys suffer from low magnetic entropy change, thus resulting in a low adiabatic temperature change. However, Fe–Ni-based alloys have low hysteresis and tunable RCP by alloying and breadth of the magnetic transition controlled by impurity- and disorder-derived distributed exchange interactions (Ucar, Craven, Laughlin, & Mchenry, 2014; Ucar et al., 2013).

As seen in Table 1 (Ucar et al., 2013), even though the compositions of Fe₇₂Ni₂₈ and Fe₇₀Ni₃₀ are different, the same Curie temperature has been obtained by varying the milling time in preparing the material (50 and 10 h, respectively). Although T_C of Fe₇₀Ni₃₀ when milled for 50 h is above room temperature, addition of Mo content can decrease it without decreasing T_C due to broadening of MCE curve because of decrease in magnetic moment and spin-up electron density (Ucar et al., 2014). Alternatively nanoclusters, e.g. Co with size of 2 nm embedded in Ni–Cu matrix, are chosen because of the tenability of their Curie temperature by varying the copper concentration (Michalski et al., 2012). Work by Mukherjee, Sahoo, Skomski, Sellmyer, and Binek (2009) shows compelling evidence of the benefits of Co/Cr superlattices through modeling and experimental data.

Work by Rong, Li, and Ping Liu (2007) on Fe_xPt_100−x shows the strong effect of controlled composition on its Curie temperature. Moreover, ordered phases with different structures and their formation in different compositional regions are discussed. Ramasamy, Mazumdar, Bennett and Gupta (2012) synthesized CuCr₂Te₄ by reducing tellurium with sodium borohydride in boiling trioctylamine yielding cube-shaped spinel nanocrystals that have room temperature T_C. Work by Sun et al. (2012) shows a reduction of 80% thermal and 60% magnetic hysteresis in MnAs_0.97P_0.03 because of 0.68% microstrain presence.

5. Suggestions for new nanostructures

Nanotechnology with its inherent flexibility of tailoring microscopic parameters unlike their bulk counterparts has its advantages (Mukherjee et al., 2009). Table 2 lists metallic and intermetallic bulk materials with magnetic entropy of at least 20 J/kg K and Curie temperature in the range of 260–340 K. As observed in case of Table 1 materials, it is anticipated that synthesizing these materials as nanostructures can achieve more than 40% improvements depending upon the composition of the materials.

6. Summary

MCE data have been gathered for all the metallic nanostructures with Curie temperature between 260 and 340 K in Table 1. When available, nanostructures were compared to their bulk counterparts to understand the effect of nanostructure on MCE properties. In all the cases observed, nanostructures have a profound positive effect on the MCE properties. Metallic nanostructures are important to be considered and further studied for use in magnetic refrigeration technology. In this regard, all metallic bulk materials with magnetic entropy of at least 20 J/kg K have been gathered in Table 2. As observed in the case of Table 1 materials, when bulk materials are synthesized as nanostructures, an improvement of greater that 40% has been reported in their MCE properties. We propose Table 2 materials be synthesized as nanostructures and their MCE properties studied.

Additional information

Funding

Funding. The authors received no direct funding for this research.

Notes on contributors

Amir Aslani

Authors are focusing their cutting-edge research at the Institute for Magnetics Research (IMR) of the George Washington University on modeling, experimental measurements, and the use of magnetic materials. The materials most commonly studied are magnetic recording media, magneto-optical media, magnetocaloric material, and magnetostrictive material. Some of the applications for such materials include computer hard drives, memories, microwave devices, magnetostrictive transducers, and magnetic refrigeration. Modeling methods employed include Preisach-based models, micromagnetics, and nonlinear finite element models. IMR has several well-equipped laboratories such as Magnetic Refrigeration Research Laboratory, Magnetic Material Testing Laboratory, and Magneto-Optics Laboratory. Past and present organizations that have cooperated with the Institute in conducting research include NIST, NSF, DOE, ONR, GE Global Research, BEP, DARPA, IBM, Fuji, and ANSYS Software.