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Application of a Coupling Agent to Improve the Dielectric Properties of Polymer-Based Nanocomposites
Published on: September 19, 2020
Flexible Polyimide Nanocomposites with dc Bias Induced Excellent Dielectric Tunability and Unique Nonpercolative
Chao Zhang1, Zhicheng Shi1, Fan Mao1
1School of Materials Science and Engineering , Ocean University of China , Qingdao 266100 , China.
Abstract:
Intrinsic metamaterials with negative- k that originated from random-structured materials have drawn increasing attention. Currently, intrinsic negative- k was mainly achieved in percolative composites by tailoring the compositions and microstructures. Herein, plasmalike negative- k was successfully achieved in multiwalled carbon nanotubes (MWCNT)/polyimide (PI) nanocomposites via applying external dc bias which exhibited excellent capability in conveniently and accurately adjusting negative- k. Mechanism analysis indicated that the localized charges at the interfaces between MWCNT and PI became delocalized after gaining energy from the dc bias, resulting in elevated concentration of delocalized charges, and hence the enhanced negative- k. Furthermore, it is surprising to observe that negative- k also appeared in multilayer nanocomposites consisting of alternating BaTiO3/PI and PI layers, in which there was no percolative conducting network. On the basis of systematic analysis, it is proposed that the unique nonpercolative negative- k resulted from the mutual competition between plasma oscillations of delocalized charges and polarizations of localized charges. Negative- k appeared once the polarizations were overwhelmed by plasma oscillations. This work demonstrated that applying dc bias is a promising way to achieve highly tailorable negative- k. Meanwhile, the observation of unique nonpercolative negative- k and the clarification of underlying mechanisms offer new insights into negative- k metamaterials, which will greatly facilitate the exploration of high-performance electromagnetic metamaterials.
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Microsoft Excel: Plotting Mean, SD, and SE
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...

