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International Journal of Solids and Structures


In this paper, the piezoresistive response (i.e. the change of resistance under the application of strain) of polymer composites reinforced by a novel material known as fuzzy fibers is characterized by using single tow piezoresistive fragmentation tests and modeled by using a 3D computational multiscale model based on the finite element analysis. In the characterization work, the fuzzy fiber tow is embedded in a dog-bone specimen infused by epoxy, with resistance and displacement measured simultaneously to obtain its piezoresistive response. An approximately linear and stable piezoresistive response is observed within the fuzzy fiber tow region yielding gauge factors on average of 0.14. Using a 3D multiscale mechanical–electrostatic coupled code and explicitly accounting for the local piezoresistive response of the anisotropic interphase region, the piezoresistive responses of the overall fuzzy fiber reinforced polymer composites are studied parametrically in an effort to provide qualitative guidance for the manufacture of fuzzy fiber reinforced polymer composites. It is observed from the model that the fuzzy fiber reinforced polymer composites with cylindrically orthotropic carbon nanotube interphase regions are dominated by the electrical tunneling effect between the nanotubes and can yield very large gauge factors while fuzzy fibers with randomly oriented carbon nanotubes in the interphase region yield smaller gauge factors as the material is electrically saturated by the carbon nanotubes. Finally, the modeling efforts provide plausible reasons for the observed small gauge factors in experiments in the form of a combination of high concentration randomly oriented carbon nanotube interphase regions separated by sparse nanotube regions along the fuzzy fiber length.

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The authors acknowledge the support of AFOSR through MURI-18 Synthesis Characterization and Modeling of Functionally Graded Hybrid Composites for Extreme Environments (Contract/Grant#: FA-9550-09-0686). The authors also acknowledge Advanced Research Computing at Virginia Tech for providing computational resources and technical support that have contributed to the results reported within this paper.





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