Ultrasensitive Embedded Sensor for Composite Joints Based on a Highly Aligned Carbon Nanotube Web

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    Herein, we present a novel approach for damage sensing in adhesively bonded joints using a carbon nanotube single layer web (CNT-SLW) which marks a significant departure from the approach of dispersing CNTs within epoxy resins. In this work, a very thin, highly aligned CNT-SLW (densified thickness ∼ 50 nm) with aerial density of 2.0 μg/cm2 was horizontally drawn from a vertically aligned CNT forest, positioned over an adhesive film, which was, in turn, placed between two non-conductive composite adherents. This was followed by the application of heat and pressure to cure the adhesive. These joints were subjected to quasi-static and cyclic loading to investigate the damage sensing performance of a CNT-SLW. The CNT-SLW sensor, placed parallel to the load direction, exhibits remarkably high cyclic stability as well as exceptionally high sensitivity to damage initiation and accumulation. The resistance increase (ΔR/Ro% ∼1633%) is significantly higher than that of adhesive sensors with dispersed CNTs/graphene reported in the literature. Morphological studies help to explain the sensing mechanism through interactions of the CNT-SLW with the evolution of micro-cracks. These results demonstrate the potential of macroscopic architectures of CNTs, with controlled orientation, for the development of high performance structural health monitoring (SHM) systems for damage detection.


    • Accepted_Manuscript_CARBON_14128

      Rights statement: Copyright 2019 Elsevier. This manuscript is distributed under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.

      Accepted author manuscript, 10 MB, PDF-document


    Original languageEnglish
    Pages (from-to)380-389
    Journal publication date19 Apr 2019
    Early online date19 Apr 2019
    Publication statusEarly online date - 19 Apr 2019

    ID: 168360208