Novel smart wearable sensors based on PVDF reinforced with CNTs for human motion monitoring
Résumé
Wearable strain sensors based on Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) reinforced with carbon nanotubes (CNTs) dispersed with Triton surfactant by solvent casting are proposed. The analysis of the electrical response shows that the conductivity increases with CNT content, as expected, whereas the addition of a high content of surfactant is more efficient at low CNT contents as it forms a more efficient electrical network. An AC analysis with Electrochemical Impedance Spectroscopy was carried out, where the variation in R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">int</sub> /R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tunnel</sub> ratio with CNT and surfactant content was analyzed. This ratio shows when the electrical pathway is saturated and the electrical transport occurs mainly through the aggregates, or when the tunneling mechanism starts to take relevance. Electromechanical analysis under tensile loading shows that the sensitivity increases with decreasing the CNT content, reaching gauge factor (GF) values of around 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> at 80-90 % strain level, higher than most of the research found in the literature. Furthermore, the electrical response under cycling loading shows similar peak and base values between consecutive cycles in a medium-term response, highlighting the robustness of the sensors. Finally, the sensors are subjected to a proof-of-concept test for finger and elbow movement monitoring, where a good agreement between the electrical and mechanical response is observed, demonstrating the applicability of the proposed materials for monitoring medium and large human movements.
Description
10.13039/501100007511-Universidad Rey Juan Carlos (Grant Number: Ref. 2986, SMARTSENS) 10.13039/501100011033-Agencia Estatal de Investigaci?n (Grant Number: Project MULTISENS, PID2022-136636OB-I00)
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