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Visualization of deformation-induced changes in carbon nanotube networks in rubber composites using lock-in thermography.

Naoyuki MatsumotoHideaki NakajimaTakahiro MorimotoTakeo YamadaToshiya OkazakiKen Kokubo
Published in: RSC advances (2023)
In this study, we used the lock-in thermography technique (LIT) to successfully visualize the single-walled carbon nanotube (CNT) networks during the tensile deformation of CNT/fluoro-rubber (FKM) composites. The LIT images revealed that the CNT network modes in CNT/FKM during strain-loading and unloading can be classified into four sites: (i) disconnection, (ii) recovery after disconnection, (iii) undestroyable, and (iv) no network. Quantitative analysis of the heat intensity of the LIT also indicated that the change in resistance during strain-loading and unloading plays a role in the balance of disconnection and reconstruction of the conductive network. We demonstrated the ability of LIT to effectively visualize and quantify the network state of the composite under deformation, and the LIT results were found to be strongly correlated with the composite properties. These results highlighted the potential of LIT as a valuable tool for composite characterization and material design.
Keyphrases
  • carbon nanotubes
  • deep learning
  • high resolution
  • convolutional neural network
  • optical coherence tomography
  • single cell
  • gold nanoparticles
  • heat stress
  • positron emission tomography
  • network analysis
  • human health