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Chemically Controllable Magnetic Transition Temperature and Magneto‐Elastic Coupling in MnZnSb Compounds
Author(s) -
Murgatroyd Philip A. E.,
Routledge Kieran,
Durdy Samantha,
Gaultois Michael W.,
Surta T. Wesley,
Dyer Matthew S.,
Claridge John B.,
Savvin Stanislav N.,
Pelloquin Denis,
Hébert Sylvie,
Alaria Jonathan
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202100108
Subject(s) - materials science , curie temperature , ferromagnetism , phase transition , synchrotron radiation , condensed matter physics , thermal conductivity , synchrotron , thermodynamics , optics , composite material , physics
Magneto‐caloric materials offer the possibility to design environmentally friendlier thermal management devices compared to the widely used gas‐based systems. The challenges to develop this solid‐state based technology lie in the difficulty of finding materials presenting a large magneto‐caloric effect over a broad temperature span together with suitable secondary application parameters such as low heat capacity and high thermal conductivity. A series of compounds derived from the PbFCl structure is investigated using a combination of computational and experimental methods focusing on the change of cell volume in magnetic and non‐magnetic ground states. Scaling analysis of the magnetic properties determines that they are second order phase transition ferromagnets and that the magnetic entropy change is driven by the coupling of magneto‐elastic strain in the square‐net through the magnetic transition determined from neutron and synchrotron X‐ray diffraction. The primary and secondary application related properties are measured experimentally, and the c/a parameter is identified as an accurate proxy to control the magnetic transition. Chemical substitution on the square‐net affords tuning of the Curie temperature over a broad temperature span between 252 and 322 K. A predictive machine learning model for the c/a parameter is developed to guide future exploratory synthesis.