Optimizing magnetic and electrical properties of MnZn ferrites via spatially engineered Ni distribution
Source abstract
Abstract
Manganese-zinc (MnZn) ferrites are widely employed in electromagnetic interference (EMI) suppression due to their favorable magnetic properties. However, their practical performance is often constrained by the inherent trade-offs among key magnetic properties, such as initial permeability (μi), impedance (Z), saturation magnetic induction (Bs), and Curie temperature (Tc). Here, we demonstrate a strategy that simultaneously enhances these properties by combining trace Ni doping with precise control of the sintering atmosphere. The low Ni concentration, together with a carefully regulated oxygen partial pressure, suppresses the diffusion driving force of Ni2+, leading to its spatially non‑uniform distribution and preferential segregation at grain boundaries. This localized Ni enrichment facilitates the formation of transgranular magnetic domains across significant crystallographic misorientation grains. Furthermore, Ni-rich grain boundaries promote the migration of Fe3+ from octahedral (B) sites to tetrahedral (A) sites, thereby increasing the A-B and A-A bond angles, shortening the corresponding bond lengths, and strengthening the super-exchange interactions within the spinel lattice. As a result of this spatially engineered Ni distribution, the optimized MnZn ferrite exhibits significantly improved properties, including an μi of 11,648 at 10 kHz, Z0 of 43 Ω·mm-1 at 1 MHz, Bs of 524 mT at 1 kHz, 1194 A·m-1, and Tc of 174 ℃, outperforming conventional MnZn ferrites. This work highlights spatial compositional engineering as a viable route to advanced soft magnetic materials for next‑generation EMI suppression technologies.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Magnetic Properties and Synthesis of Ferrites · Electromagnetic wave absorption materials · Metallic Glasses and Amorphous Alloys
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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