Mace-like carbon fiber/ZnO nanorod composite derived from Typha orientalis for lightweight and high-efficient electromagnetic wave absorber
Source abstract
Abstract
Inspired by nature, biomass-derived carbon has become a representative electromagnetic wave absorption (EMA) material due to its advantages of economy, renewability, and environmental protection. However, it is a challenge to make further breakthroughs in effective absorption bandwidth (EAB) and filling ratio. In this work, utilizing sustainable biomass Typha orientalis as a template, the hierarchically mace-like carbon fiber/ZnO nanorod composite (BDCFs@ZnO) was reasonably constructed to obtain the enhanced EMA performance for the first time. As a typical dielectric-loss absorber, the hollow structure of BDCFs and the construction of the interconnected conductive network contribute to conduction loss and multiple reflections. The ZnO nanorods grew uniformly on the surface of BDCFs, favoring impedance matching and interface polarization. Moreover, the oriented ZnO nanorods possess abundant inherent polar surfaces, promoting polarization loss. Remarkably, BDCFs@ZnO exhibits an exceptional RL of − 62.35 dB at 14.12 GHz under a matching thickness of 2.29mm when the filling ratio is only 15 wt%. Additionally, the EAB can reach 6.8 GHz, covering almost the whole Ku band. The excellent EMA performance stems from the synergetic action of conduction loss, multiple reflection, dipole polarization, and interfacial polarization. This work provides an essential strategy for the construction of hierarchical carbon-based materials and inspires the design of biomass-derived electromagnetic wave absorbers. Mace-like carbon fiber/ZnO nanorod composites derived from Typha orientalis as ultralight and high-performance electromagnetic wave absorber
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Advanced Antenna and Metasurface Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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