journal article·2024-03Open access

Significantly improved near-field communication antennas based on novel Ho 3+ and Co 2+ ions co-doped Ni–Zn ferrites

Pao Yang, Zhiqing Liu, Hongbin Qi, Xiuli Fu, Zhijian Peng

Journal of Advanced Ceramics· Volume 13· Issue 3

Abstract

In near-field communication (NFC) antennas, soft magnetic ferrites are usually applied as substrate to reduce eddy current loss and increase magnetic field coupling. For this purpose, the applied ferrites are required to have high permeability and saturation magnetization together with low magnetic loss and dielectric loss. However, for most soft magnetic ferrites, it is difficult to meet all of the requirements. Herein novel Ni-Zn ferrite ceramics co-doped by Ho3+ and Co2+ ions with chemical formula Ni0.5-xZn0.5Ho0.02CoxFe1.98O4 (x=0-0.2) were designed and prepared to balance these needs on the basis of molten salt synthesis with metal nitrates as the raw materials and KOH as the precipitation agent and molten salt precursor. After the substitution of Ho3+, the saturation magnetization and initial permeability decrease, but with the further doping of Co2+, the saturation magnetization gradually increases, while the initial permeability continues to decrease. When x=0.1, the sample will have the lowest dielectric constant, magnetic and dielectric loss, as well as the highest Curie temperature (305 ºC). Moreover, the acquired Ni-Zn ferrites have been applied simulatively in NFC antennas, revealing that the device manufactured with the optimal Ni0.4Zn0.5Ho0.02Co0.1Fe1.98O4 ferrite ceramics would have significantly improved performance at 13.56 MHz with low leakage and long transmit distance of magnetic field. Therefore, the Ni0.4Zn0.5Ho0.02Co0.1Fe1.98O4 ferrite ceramics would be a good candidate for NFC antenna substrates.
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Research context

OpenAlex topics: Full-Duplex Wireless Communications · Electromagnetic wave absorption materials · Magnetic Properties and Synthesis of Ferrites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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