journal article·2020-05

Tuning the impedance matching characteristics of microwave absorbing paint in X-band using copper particles and polypyrrole coating

Qiang Gu, Mojtaba Jafarian, Seyyed Salman Seyyed Afghahi, Yomen Atassi, Rama Al-Qassar

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Abstract

Abstract A new multifunctional microwave absorbing material is designed. Two types of ferrites were synthesized, namely soft NiCoTi ferrite and hard SrCoTi ferrites, by sol-gel auto combustion method. Then, the ferrites along with an appropriate quantity of micro-copper particles were coated by polypyrrole using in situ chemical polymerization of pyrrole. The resulted material was hosted into a polyurethane matrix to prepare a microwave absorbing paint. The accumulative roles of the different components of the microwave absorber were investigated. The permittivity and permeability were recorded in the X-band and the reflection loss of the material at different thicknesses was estimated. The structure of the prepared microwave absorbing material was investigated using XRD and FTIR spectroscopy. VSM was used to record the samples’ magnetic hysteresis loops. The morphology of the absorber was studied using SEM. The new prepared microwave absorbing composite material was compared to a simple mixture of its components and the importance effect of the interfacial polarization was highlighted. The microwave absorption properties were measured in the X-band range as a function of absorber thickness, Ferrites/PPY ratio and copper percentage. By changing Ferrites/PPY ratio the microwave absorption performance was tuned. The optimal copper percentage in the composite was optimized and it was found that 10 %w/w of copper widens the -10 dB bandwidth to 3 GHz and enhances the maximum reflection loss to -24 dB.
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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