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Role of MWCNTs Loading in Designing Self-Sensing and Self-Heating Structural Elements.

Liberata GuadagnoRaffaele LongoFrancesca AlibertiPatrizia LambertiVincenzo TucciRoberto PantaniGiovanni SpinelliMichelina CatauroLuigi Vertuccio
Published in: Nanomaterials (Basel, Switzerland) (2023)
This work proposes nanocomposites with carbon nanotubes characterized by self-sensing and self-heating properties. Recently, a growing interest in these two properties has been found in many industrial sectors, especially in the aerospace and automotive fields. While the self-sensing function allows diagnosing the presence of micro-damage in the material thanks to the detection of residual resistance, the self-heating function is exploited to properly tune the heating performance in terms of the heating rate and final temperature values. An electrical percolation value of around 0.5% by weight of carbon nanotubes was found by electrical characterization. The AC conductivity of the nanocomposites, in the range of 100 Hz to 1 MHz, evidences that beyond a CNTs amount of 0.5% wt/wt, they are characterized by a purely resistive behavior. The self-sensing analysis displayed a gauge factor value of 4.1. The solid thermal stability up to 300 °C makes the material suitable as a heating element at high temperatures. SEM investigations and temperature maps evidence a good dispersion of the conductive filler in the epoxy matrix and, consequently, good isotropy in heat distribution. As regards the trend of electrical resistance by varying the temperature, the electro-thermal investigation has shown the presence of both Positive Temperature Coefficient (PTC) and Negative Temperature Coefficient (NTC) behaviors with a predominance of NTC as soon as the temperature becomes closer to the glass transition temperature of the epoxy resin.
Keyphrases
  • carbon nanotubes
  • oxidative stress
  • computed tomography
  • weight loss
  • diffusion weighted imaging
  • magnetic resonance
  • high resolution
  • ultrasound guided
  • heat stress
  • real time pcr
  • data analysis