journal article·2021-06

A review on recent advances in carbon-based dielectric system for microwave absorption

Liru Cui, Xijiang Han, Fengyuan Wang, Honghong Zhao, Yunchen Du

Journal of Materials Science· Volume 56· Issue 18
Publisher page

Abstract

Microwave absorbing materials (MAMs), which have been highly developed in the past two decades, are being regarded as a kind of functional materials to combat electromagnetic (EM) pollution, because they can provide sustainable energy conversion rather than simple reflection of incident EM waves. Although traditional magnetic materials can dissipate EM energy effectively, they always suffer from some intrinsic drawbacks, such as high density, easy corrosion, and skin effect, which make them unpopular in many practical applications. Therefore, the rational design of pure dielectric system without any magnetic components is becoming a new frontier topic for MAMs. Among various candidates, carbon-based dielectric system almost dominates the development of non-magnetic MAMs. This review presents a comprehensive introduction on the recent advances of carbon-based dielectric system composed of carbon materials and other dielectric components, including metal oxides/carbon, metal sulfides/carbon, conductive polymers/carbon, carbides/carbon, carbon/carbon, and various ternary carbon-based dielectric composites. Thanks to the synergistic effects between different components and the elaborate microstructure design, these carbon-based dielectric composites can produce superior microwave absorption performance to traditional magnetic materials. Moreover, the challenges and prospects are also proposed to indicate some new insights for the further research of carbon-based dielectric system.
Source: openaire · 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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