Abstract
The use of graphene and other carbon nanoparticles is now of interest for developing chemical (gas and compounds detectors) and physical sensors. In this work, a graphene nanoplatelet (GNP)-PDMS sensor is proposed. More specifically, its strain-sensing capabilities under consecutive cycles as well as the crack propagation mechanisms are widely analyzed. First, an analysis of the electrical properties shows that the increase of the GNP content leads, as expected, to an increase of the electrical conductivity, ranging from values around 10−3 to 1 S/m for 5 and 11 wt.% samples. The analysis of crack propagation monitoring capabilities shows an exceptional sensitivity of the proposed flexible sensors, with a highly exponential behavior of the electrical resistance due to the prevalent breakage of the electrical pathways as crack propagation occurs. Furthermore, the analysis of the electrical response under cyclic load proves a very high robustness, with a similar response when comparing different cycles and an electrical sensitivity that increases when decreasing the GNP content (from 15–25 to 25–50 at 7 and 11 wt.% GNP content, respectively), a fact that is explained by the prevalence of tunneling mechanisms at low contents. Finally, a proof-of-concept of human motion monitoring by the detection of neck, wrist and facial movements is successfully achieved, indicating the high applicability of the proposed sensors.
Highlights
The use of nanoparticle-based materials is nowadays gaining a great deal of attention due to their interesting properties and functionalities
It can be observed that the electrical conductivity increased with graphene nanoplatelet (GNP) content, as expected, due to the creation of more prevalent electrical pathways due to the higher number of nanoparticles
The electrical conductivity measurements are shown in the graph of Figure to the higher prevalence of the shear forces involved during the dispersion procedure, be observed that the electrical conductivity increased with content, a very high GNP concentration could lead to a very aggressive disaggregation of theseas expec nanoparticles, inducing a partial breakage electrical of themselves and affecting to the creation of more prevalent pathways duethe to effectiveness the higher ofnumber the dispersion procedure
Summary
The use of nanoparticle-based materials is nowadays gaining a great deal of attention due to their interesting properties and functionalities. Graphene has been used as a base material for field-effect transistor sensors, showing a high sensitivity [4], and its use for the detection of volatile organic compounds (VOCs) has been explored, showing its capability for VOCs detection in a selective way [5]. Their use as chemiresistors for the detection of NO2 have been explored, reaching very high chemi-resistive responses [6]
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