4.8 Article

A Composite Fabrication Sensor Based on Electrochemical Doping of Carbon Nanotube Yarns

Journal

ADVANCED FUNCTIONAL MATERIALS
Volume 26, Issue 39, Pages 7139-7147

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.201602949

Keywords

-

Funding

  1. European Union [678565, 310184]
  2. MINECO (Spain) [MT2012-37552-C03-02, MAT2015-62584-ERC, RyC-201415115]
  3. Madrid regional government [S2013/MIT-3007 MAD2D]

Ask authors/readers for more resources

This work shows evidence of conventional liquid and polymer molecules doping macroscopic yarns made up of carbon nanotubes (CNT), an effect that is exploited to monitor polymer flow and thermoset curing during fabrication of a structural composite by vacuum infusion. The sensing mechanism is based on adsorption of liquid/polymer molecules after infiltration into the porous fibers. These molecules act as dopants that produce large changes in longitudinal fiber resistance, closely related to the low density of carriers near the Fermi level of bulk samples of CNT fibers, reminiscent of their low-dimensional constituents. A 25% decrease in fiber resistance upon exposure to electron-donor radicals formed during epoxy vinyl ester polymerization is shown as an example. At later stages of curing the matrix undergoes shrinkage and applies a compressive stress to the fibers. The resulting sharp increase in electrical resistance provides a mechanism for detection of the matrix gel point. The kinetics of resistance change during polymer ingress are related to established models for macromolecular adsorption, thus also enabling prediction of polymer flow. This is demonstrated for vacuum infusion of a 150 cm 2 glass fiber laminate composite, with the CNT fiber yarns giving accurate prediction of macroscopic resin flow according to Darcy's law.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available