Hollow microsphere-infused porous poly(vinylidene fluoride)/multiwall carbon nanotube composites with excellent electromagnetic shielding and low thermal transport
Source abstract
Abstract
Hollow glass microspheres (HGMs) offer advantages such as high chemical stability, light weight, and low cost and were firstly introduced to prepare functional polymer composites of multi-walled carbon nanotubes (MWCNTs) and poly(vinylidene fluoride). When preparing porous composites via hot compaction and selective etching, embedding of HGMs into the polymer matrix promotes the continuity of the conducting pathways in the material system and thus enhances electrical conductivity. Furthermore, HGMs play an important role in multiple scattering and reflecting the incident waves and the synergistic effect between HGMs and the MWCNTs conductive network greatly enhances the electromagnetic interference (EMI) shielding properties of the composites. In this paper, we show that with 10 wt% MWCNT loading, polymer composite samples containing only 2 wt% HGMs exhibit an average EMI shielding effectiveness (SE) of 43.03 dB over the frequency of 8.2–12.4 GHz. This SE value is higher than in samples with no HGMs (25.27 dB). Analysis of the measured scattering parameters reveals that microwave absorption is the primary reason for enhanced EMI SE. The introduction of HGMs also decreases the thermal conductivity of the composites by reducing the active surface area that promotes efficient heat transfer. With the addition of 2 wt% HGMs, the thermal conductivity of the composites reduces by 46.9%, from 0.305 to 0.162 W(mK)−1. This work provides a promising technique to prepare good-quality EMI shielding materials with lower thermal conductivity to meet the requirements of different applications.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Advanced Antenna and Metasurface Technologies · Dielectric materials and actuators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
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