Effective regulation of EMI properties and shielding mechanism of electrospun CNF/Co-Ni composites via organometallic precursor concentration
Source abstract
Abstract
Efficient thin-thickness electromagnetic interference (EMI) shielding materials are a significant technological challenge. This study takes a unique approach to this issue by fabricating ultra-thin composite mats with superior EMI SE shielding efficiency, covering X- and S-bands. These mats exhibit tunable electrical and magnetic behavior, achieved through carbon nanofibers (CNF) and CoNi-based nanofillers. The process involves the formation of fibrous nanocomposites with a CoNi phase, which are created by electrospinning and heat-treating polyacrylonitrile fibers containing a metal-organic precursor in the form of metallic acetylacetonates (Me(Acac)) up to 4.5 % wt. The addition of acetylacetonates significantly enhances the conductivity of the nanofibers, with a maximum value observed for 3 % CoNi(Acac)2 – σ = 1570.6 S/m, more than double that of pure CNF (σ = 733.6 S/m). A comprehensive study of the relationship between the concentration of the metal precursor and the quality/composition of CoNi dispersion, CNF structure, EMI SE, magnetization, and conductivity revealed the best EMI properties of the mat. Even a small concentration of Me-based particles significantly improved EMI SE from 25.3 dB to 56.7 dB in the 5 GHz band at a sample thickness of 50 µm. The 3 % wt organometallic precursor (Me(acac)) sample achieved the highest EMI SE, conductivity, and magnetization. However, a higher concentration of Me(Acac) in the samples led to the inhibition of the EMI saturation effect due to the deterioration of individual CNF integrity in the sample, greater susceptibility of CoNi to oxidation, and loss of nanoparticles from the material.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Electrical Contact Performance and Analysis · Tribology and Wear Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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