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A novel hierarchically porous magnetic carbon derived from biomass for strong lightweight microwave absorption

Zhao, Huanqin, Cheng, Yan, Lv, Hualiang, Ji, Guangbin, Du, Youwei·2019·Carbon

Abstract

Abstract Recent findings reveal that biomass material sometimes exhibits novel microstructure, in particular for some heterostructures and microtubular channels, which is hard to be realized by conventional chemical synthesis. Utilizing of intrinsic microstructure characteristic of biomass in nature to optimize microstructure and material component is regarded as a potential way to deal with ever-growing electromagnetic (EM) interference issue. This research taking rice as an example, a pre-treatment is conducted by dipping and annealing to obtain porous carbon and simultaneously incorporation of beneficial magnetic component. By optimizing of microstructure and material component, the fabricated hierarchically porous magnetic carbon (HPMC) material shows excellent EM wave performance with minimum reflection loss (RLmin) of −52 dB and wide effective absorption bandwidth (EAB) of 5 GHz at low filler content of 15 wt%. This work paves the way for development of biomass as a low-cost, green and renewable high-performance carbon-based absorber.

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Metadata

Authors
Zhao, H., Cheng, Y., Lv, H., Ji, G., & Du, Y.
Venue
Carbon
Publisher
Elsevier BV
Published
2019-02
Volume
142
Pages
245-253
ISSN
0008-6223 (ISSN-L)
OpenAlex
W2897514316
Cited by
504
References
0
FWCI
17.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A novel hierarchically porous magnetic carbon derived from biomass for strong lightweight microwave absorption. https://app.cochat.ai/papers/10.1016/j.carbon.2018.10.027
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Zhao, H., Cheng, Y., Lv, H., Ji, G., & Du, Y. (2019). A novel hierarchically porous magnetic carbon derived from biomass for strong lightweight microwave absorption. Carbon. https://doi.org/10.1016/j.carbon.2018.10.027

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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.