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Cited by 991

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Electromagnetic microwave absorption theory and recent achievements in microwave absorbers

Zeng, Xiaojun, Cheng, Xiaoyu, Yu, Ronghai, Stucky, Galen D.·2020·Carbon

Abstract

Abstract Great effort has recently been made to develop unique nanomaterials with superior reflection loss (RL), thin thickness, wide bandwidth and low density to improve their efficiency in electromagnetic (EM) microwave absorption, while maintaining a simple manufacturing process. The use of traditional nanomaterials with magnetic loss or dielectric loss, of course, not only makes the last requirement hard to meet but also restricts the thickness of absorber, and limits mass production. Recently, some promising nanomaterials with excellent conduction loss and polarization loss have been explored which promise to enable unique nanostructures with multiple reflections and interfacial polarization. Herein, we discuss the EM microwave absorption theory. Then, we review recent achievements in manufacturing EM microwave absorption materials, particularly focusing on the unique and key factors in design and control of structures and components. In addition, current challenges are presented, and prospects for future development in this rapidly blossoming field are discussed.

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Metadata

Authors
Zeng, X., Cheng, X., Yu, R., & Stucky, G. D.
Venue
Carbon
Publisher
Elsevier BV
Published
2020-10
Volume
168
Pages
606-623
ISSN
0008-6223 (ISSN-L)
OpenAlex
W3043288153
Cited by
991
References
3
FWCI
72.93
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Electromagnetic microwave absorption theory and recent achievements in microwave absorbers. https://app.cochat.ai/papers/10.1016/j.carbon.2020.07.028
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Zeng, X., Cheng, X., Yu, R., & Stucky, G. D. (2020). Electromagnetic microwave absorption theory and recent achievements in microwave absorbers. Carbon. https://doi.org/10.1016/j.carbon.2020.07.028

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.