Analytical modeling of magnetocaloric effect in dense nanoparticle systems

• Published in: Nano select Vol. 5; no. 6
• Main Authors: Bohra, Murtaza, Alman, Vidya, Khan, Mohd Ataullah, Singh, Vidyadhar, Toulkeridou, Evropi, Grammatikopoulos, Panagiotis
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.06.2024; Wiley-VCH
• Subjects: Alloys; analytical models; Cost analysis; Curie temperature; Curie temperature distribution; Inhomogeneity; magnetic interactions; Magnetic properties; Magnetic transitions; magnetocaloric effect; Nanoparticles; Normal distribution; Particle size; Particle size distribution; Silicon nitride; Temperature; Temperature dependence; Thin films
• ISSN: 2688-4011; 2688-4011
• DOI: 10.1002/nano.202300196

Determining the magnetocaloric effect (MCE) in dense nanoparticle systems (DNSs) poses a challenge due to the increased complexity of matter at the nanoscale. Given the interparticle magnetic interactions, diverse particle size and shape distributions, and the presence of inhomogeneous magnetic phases, selecting a suitable phenomenological model is essential to describe the temperature dependence of magnetic behavior in DNSs. Herein, we chose a cost‐effective Ni100‐xCrx DNS with adjustable magnetic transitions to showcase the resilience of the MCE across a broad temperature range (147–614 K). While the hyperbolic tangent model appears more fitting for materials with a single Curie temperature (TC), such as its parent bulk alloys, in the presence of a TC distribution a Gaussian distribution model proves to be better suited for DNSs. The latter model yields a magnetic entropy change, ΔSmax = 0.09–0.15 J kg‐K−1 in the DNS at a tiny field of 0.1T. The correlations between the broadening of the MCE peak and TC distribution are attributed to the particle size distribution and chemical inhomogeneity present in the DNS, paving the way for fine‐tuning MCE‐related properties such as the relative cooling power (13.17–33.45 J kg−1) and adiabatic temperature change (0.03–0.17 K). Our methodology not only enhances the potential for designing innovative MCE materials with broader operating ranges but also validates the universality of our phenomenological model for other families of nanocrystalline/nanogranular oxides/alloys thin films. Nano‐complexity challenges magnetocaloric effect (MCE) prediction in dense nanoparticle systems (DNSs). We demonstrate a cost‐effective Ni100‐xCrx DNS, showcasing strong MCE across a broad temperature range (147–614 K). A Gaussian model excels over a hyperbolic tangent one due to a Curie temperature (TC) distribution in DNS, yielding ΔSmax = 0.09–0.15 J kg‐K−1 at 0.1 T. MCE peak broadening correlates with particle size distributions/chemical ordering inhomogeneities.