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Aligned multi-walled carbon nanotube-reinforced composites: processing and mechanical characterization

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Thostenson, Erik T, Chou, Tsu-Wei·2002·Journal of Physics D: Applied Physics

Abstract

Carbon nanotubes have been the subject of considerable attention because of their exceptional physical and mechanical properties. These properties observed at the nanoscale have motivated researchers to utilize carbon nanotubes as reinforcement in composite materials. In this research, a micro-scale twin-screw extruder was used to achieve dispersion of multi-walled carbon nanotubes in a polystyrene matrix. Highly aligned nanocomposite films were produced by extruding the polymer melt through a rectangular die and drawing the film prior to cooling. Randomly oriented nanocomposites were produced by achieving dispersion first with the twin-screw extruder followed by pressing a film using a hydraulic press. The tensile behaviour of the aligned and random nanocomposite films with 5 wt.{%} loading of nanotubes were characterized. Addition of nanotubes increased the tensile modulus, yield strength and ultimate strengths of the polymer films, and the improvement in elastic modulus with the aligned nanotube composite is five times greater than the improvement for the randomly oriented composite.

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Metadata

Authors
Thostenson, E. T., & Chou, T.-W.
Venue
Journal of Physics D: Applied Physics
Published
2002-08-07
Volume
35
Issue
16
Pages
L77-L80
ISSN
0022-3727 (ISSN-L)
OpenAlex
W2083583146
Cited by
685
References
0
FWCI
15.21
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Aligned multi-walled carbon nanotube-reinforced composites: processing and mechanical characterization. https://app.cochat.ai/papers/10.1088/0022-3727/35/16/103
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Thostenson, E. T., & Chou, T.-W. (2002). Aligned multi-walled carbon nanotube-reinforced composites: processing and mechanical characterization. Journal of Physics D: Applied Physics. https://doi.org/10.1088/0022-3727/35/16/103

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