Ferromagnetic-Antiferromagnetic Coupling in Gas-Phase Synthesized M(Fe, Co, and Ni)-Cr Nanoparticles for Next-Generation Magnetic Applications.

Combining ferromagnetic-antiferromagnetic materials in nanoalloys (i.e., nanoparticles, NPs, containing more than one element) can create a diverse landscape of potential electronic structures.

Towards Production of Cost-Effective Modification of SmCo-Type Alloys Suitable for Permanent Magnets.

SmCo constitutes one of the strongest classes of permanent magnets, which exhibit magnetocrystalline anisotropy with uniaxial character and enormous energy and possess high Curie temperature. However, the performance of SmCo permanent magnets is hindered by a limited energy product and relatively high supply risk. Sm is a moderately expensive element within the lanthanide group, while Co is a more expensive material than Fe, making SmCo-based permanent magnets among the most expensive materials in the group.

On Structural and Magnetic Properties of Substituted SmCo Materials.

SmCo is a well-established material in the permanent magnet industry, a sector which constantly gains market share due to increasing demand but also suffers from criticality of some raw materials. In this work we study the possibility of replacement of Sm with other, more abundant rare earth atoms like Ce-La. These raw materials are usually called "free" rare-earth minerals, appearing as a by-product during mining and processing of other raw materials.

, artificial neural network predictions and experimental synthesis of mischmetal alloying in Sm-Co permanent magnets.

The use of the mischmetal alloy, comprised of La and Ce in 1 : 3 ratio, as a partial substitute for Sm in the CaCu-type structure is explored, as a means for the search of viable alternatives for permanent magnets that require fewer steps in the rare earth separation processing. The structural and magnetic properties of the introduced stoichiometry, containing 50% less Sm, are compared to the ones of the SmCo, LaCo and CeCo binary compounds by means of simulations. The capability of artificial neural networks to accurately predict the relationship between structure and total magnetization from DFT calculations in the supercell approach that was employed, is also demonstrated.