journal article·2022-12

Quantum dots with Mott-Schottky effect embedded in crystal-amorphous carbon for broadband electromagnetic wave absorption

Ye Liao, Yifeng E, Xin Zhou, Gaihua He

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Abstract

The porous carbon material embedded with tunable quantum dots is prepared with the DC arc plasma technology for highlighted microwave absorbing material. The coupling transition metal oxide MnO and magnetic Fe into carbon matrix constructs a heterogeneous core-shell composite. Magnetic iron can improve the magnetic response ability. Nanoscale MnO not only helps produce more dipole polarization and relaxation, but also can adjust the catalytic effect of the magnetic iron. Therefore, the carbon material exists in two forms, with adjustable crystallinity, forming a unique crystal-amorphous structure. Synchronously, the integration between metal and semiconductor can promote the charge-transfer and transportation, highly enhancing the microwave absorption due to the Mott-Schottky effect. In addition, formation of heterostructure with MnO, Fe and carbon leads to mismatching of electron spins, forming a large amount of magnetic moments, which enhances the magnetic loss capability. The material shows excellent microwave absorption properties and tunable frequency. The maximum reflection loss reaches − 60.85 dB and effective absorption bandwidth achieves 5.1 dB at 2 mm. Besides, the size of Fe/MnO nanocrystal can be confined by regulating the feed ratio and reaction atmosphere. This research might shed a new light on the improvement of novel microwave absorption materials. Synopsis With the explosive advance of information technology, electromagnetic pollution and interference issues occur increasingly, threating biological health and environment security.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Electromagnetic wave absorption materials · Carbon and Quantum Dots Applications · Advanced Antenna and Metasurface Technologies

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

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