journal article·2024-06

Ni-doped Co3O4 with micro-nano porous structure as a highly efficient microwave absorber

Min Zhang, Cheng Qian, Ruotong Zhu, Zhengzhou Jin, Yongxing Zhang, Bo Yang, Weiwei Zhang

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Abstract

This article employs a hydrothermal method combined with a calcination process to prepare Co 3O 4 microsheets with varying Ni doping concentrations. Microstructural analysis reveals that the obtained samples are single-phase, free from other impurities, and exhibit a unique self-assembled porous micro-nanostructure. The micron-sized secondary aggregates consist of small nanometer-sized particles with many voids. The nanoscale pores between the nanoparticles contribute to the dissipation of incident microwaves. The dielectric and magnetic properties of Co 3O 4 microplates can be effectively tuned by adjusting the Ni doping concentration. Comparative results indicate that the Ni-doped Co 3O 4 microsheets with 0.03 mol% Ni exhibit the minimum reflection loss (RL min) of −34.7 dB at 16.56 GHz, with a thickness of just 1.4 mm. Simultaneously, the effective absorption bandwidth (EAB) reaches 4.32 GHz within the frequency range of 13.68–18 GHz. With an absorber thickness of 1.5 mm, the EAB can reach 5.2 GHz from 12.8 to 18 GHz. By adjusting the matching thickness of the absorber in the range of 1.4–4.5 mm, the EAB of Ni-doped Co 3O 4 can cover multiple frequency bands from 3.6 to 18 GHz. The outstanding microwave absorption performance primarily stems from the unique porous micro-nanostructure and enhanced dielectric and magnetic losses. The results provide an effective component regulating strategy to promote microwave absorption, meanwhile suggest that the low-cost Ni-doped Co 3O 4 is a promising microwave absorption material.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Electromagnetic wave absorption materials · Advanced Antenna and Metasurface Technologies · Metamaterials and Metasurfaces Applications

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

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