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Cited by 171

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Fabrication and property prediction of conductive and strain sensing TPU/CNT nanocomposite fibres

Bilotti, Emiliano, Zhang, Rui, Deng, Hua, Baxendale, Mark, Peijs, Ton·2010·Journal of Materials Chemistry

Abstract

In this study, thermoplastic polyurethane (TPU) fibres containing multi-walled carbon nanotubes (MWNTs) and fabricated via an extrusion process were demonstrated to possess a tuneable level of electrical conductivity. A simple approach based on the time–temperature superposition applied to the electrical conductivity of carbon nanotube (CNT) percolating in a thermoplastic polyurethane (TPU) melt was also developed to predict the conductivity of the nanocomposite fibres. The observation of Arrhenius dependence of zero-shear viscosity and the assumption of simple inverse proportionality between the variation of conductivity, due to network formation, and viscosity allow a universal plot of time variation of conductivity to be composed, which is able to predict the conductivity of the extruded fibres. The same TPU/CNT fibres were also demonstrated to possess good strain sensing abilities, which makes them good candidates for applications in smart textiles.

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Metadata

Authors
Bilotti, E., Zhang, R., Deng, H., Baxendale, M., & Peijs, T.
Venue
Journal of Materials Chemistry
Published
2010
Volume
20
Issue
42
Pages
9449
ISSN
  • 0959-9428 (ISSN-L)
  • 1364-5501
OpenAlex
W2052607169
Cited by
171
References
0
FWCI
5.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fabrication and property prediction of conductive and strain sensing TPU/CNT nanocomposite fibres. https://app.cochat.ai/papers/10.1039/c0jm01827a
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Bilotti, E., Zhang, R., Deng, H., Baxendale, M., & Peijs, T. (2010). Fabrication and property prediction of conductive and strain sensing TPU/CNT nanocomposite fibres. Journal of Materials Chemistry. https://doi.org/10.1039/c0jm01827a

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.