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Journal of Materials Science

, Volume 49, Issue 24, pp 8317–8327 | Cite as

Investigation of the internal structure of hemp fibres using optical coherence tomography and Focused Ion Beam transverse cutting

  • Vincent PlacetEmail author
  • Jérémy Méteau
  • Luc Froehly
  • Roland Salut
  • M. Lamine Boubakar
Original Paper

Abstract

The use of plant fibres in composite applications requires an efficient characterisation of their mechanical properties and thus an accurate description of their internal structure. The review of literature points out that there is still a lack of data on the organisation and structure of bast fibres. In this study, we propose to investigate the internal structure of hemp fibres using two experimental techniques: Focused Ion Beam (FIB) microscopy and optical coherence tomography (OCT). Results indicate that OCT, a non-destructive and non-invasive technique, is a powerful technique to quickly and easily describe the internal structure of fibres and also to discriminate single fibres from bundle of fibres. In this paper, we also show that among technical hemp fibres and for a same range of external diameters (of about 20–30 μm), two types of internal structures can be observed: (i) elementary fibres with a thick wall and a small lumen and (ii) bundle of small fibres with an external diameter of a few microns. According to data of literature, these two structures were identified as being respectively primary fibres and bundle of secondary fibres. This result is of great importance for the mechanical characterization of the bast hemp fibres. Indeed, this means that during the test campaigns, the batch of isolated fibres is undoubtedly composed of both single primary fibres and bundle of secondary fibres. It certainly participates to the high scattering in results.

Keywords

Optical Coherence Tomography Hemp Middle Lamella Flax Fibre Polarise Light Microscopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was partly supported by the French RENATECH network and its FEMTO-ST technological facility.

Supplementary material

Supplementary material 1 (MPG 74803 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Vincent Placet
    • 1
    Email author
  • Jérémy Méteau
    • 2
  • Luc Froehly
    • 2
  • Roland Salut
    • 2
  • M. Lamine Boubakar
    • 1
  1. 1.Department of Applied Mechanics, FEMTO-ST Institute, UMR CNRS 6174University of Franche-ComtéBesançonFrance
  2. 2.Department of Optics, FEMTO-ST Institute, UMR CNRS 6174University of Franche-ComtéBesançonFrance

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