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Molecular mechanics of binding in carbon-nanotube–polymer composites

Lordi, Vincenzo, Yao, Nan·2000·Journal of Materials Research

Abstract

Nanoscale composites have been a technological dream for many years. Recently, increased interest has arisen in using carbon nanotubes as a filler for polymer composites, owing to their very small diameters on the order of 1 nm, very high aspect ratios of 1000 or more, and exceptional strength with Young's modulus of approximately 1 TPa. A key issue for realizing these composites is obtaining good interfacial adhesion between the phases. In this work, we used force-field based molecular mechanics calculations to determine binding energies and sliding frictional stresses between pristine carbon nanotubes and a range of polymer substrates, in an effort to understand the factors governing interfacial adhesion. The particular polymers studied were chosen to correspond to reported composites in the literature. We also examined polymer morphologies by performing energy-minimizations in a vacuum. Hydrogen bond interactions with the ∏-bond network of pristine carbon nanotubes were found to bond most strongly to the surface, in the absence of chemically altered nanotubes. Surprisingly, we found that binding energies and frictional forces play only a minor role in determining the strength of the interface, but that helical polymer conformations are essential.

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Metadata

Authors
Lordi, V., & Yao, N.
Venue
Journal of Materials Research
Published
2000-12
Volume
15
Issue
12
Pages
2770-2779
ISSN
  • 0884-1616 (ISSN-L)
  • 0884-2914
  • 2044-5326
OpenAlex
W2041312022
Cited by
377
References
0
FWCI
10.48
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Molecular mechanics of binding in carbon-nanotube–polymer composites. https://app.cochat.ai/papers/10.1557/jmr.2000.0396
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Lordi, V., & Yao, N. (2000). Molecular mechanics of binding in carbon-nanotube–polymer composites. Journal of Materials Research. https://doi.org/10.1557/jmr.2000.0396

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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.