A Simple Model Relating Gauge Factor to Filler Loading in Nanocomposite Strain Sensors

Abstract

Conductive nanocomposites are often piezoresistive, displaying significant changes in resistance on deformation, making them ideal for use as strain and pressure sensors.Such composites typically consist of ductile polymers filled with conductive nanomaterials, such as graphene nanosheets or carbon nanotubes, and can display sensitivities, or gauge factors, which are much higher than those of traditional metal strain gauges. However, their development has been hampered by the absence of physical models which could be used to fit data, or to optimise sensor performance. Here we develop a simple model which results in equations for nano-composite gauge factor as a function of both filler volume fraction and composite conductivity. These equations can be used to fit experimental data, outputting figures of merit, or predict experimental data once certain physical parameters are known. We have found these equations to match experimental data, both measured here and extracted from the literature, extremely well. Importantly, the model shows the response of composite strain sensors to be more complex than previously thought and shows factors other than the effect of strain on the interparticle resistance to be performance-limiting.

Printable G‐Putty for Frequency‐ and Rate‐Independent, High‐Performance Strain Sensors

“…It has previously been reported that strain sensors often show negative correlations between conductivity and gauge factor. [29,32] As shown in Figure 2e, this is also the case for both bulk and sprayed G-putty. However, this graph clearly highlights the differences between these systems and shows that sprayed G-putty can achieve a given G at much higher conductivity than bulk G-putty.…”

“…Graphs of R/R0 versus increasing , in the low strain region, are shown in figure 2c for sprayed G-putty films with three different graphene mass fractions. Average gauge factors are plotted versus graphene mass fraction in figure 2d and show the usual [17,32] increase with decreasing filler loading, reaching G~110 for mass fractions below 5%, considerably higher than values of ~40 demonstrated previously for graphene networks deposited via swelling and soaking. [33] We have used a new model reported by Garcia et al [32] to compare the G vs.  data for bulk and sprayed G-putty as reported in the SI.…”

“…Average gauge factors are plotted versus graphene mass fraction in figure 2d and show the usual [17,32] increase with decreasing filler loading, reaching G~110 for mass fractions below 5%, considerably higher than values of ~40 demonstrated previously for graphene networks deposited via swelling and soaking. [33] We have used a new model reported by Garcia et al [32] to compare the G vs.  data for bulk and sprayed G-putty as reported in the SI. This analysis suggests the impact of strain on intersheet charge transfer to be particularly important in sprayed films while strain-induced changes in network structure are dominant in bulk G-putty.…”

Electromechanical Performance of Strain Sensors Based on Viscoelastic Conductive Composite Polymer Fibers

“…Below the percolation threshold, experimental data can be described by eq We fitted the conductivity data to eqs and for varying the volume fractions of CB (Figure a), and the percolation threshold was predicted to be 11.85 vol %. Furthermore, a linearized log­(σ) – log­( P – P c ) plot of fiber conductivity (σ) vs volume fractions of CB above the percolation threshold was plotted to confirm the accurate fitting of our experimental findings to eq , which is in agreement with recent findings (Figure b) . Despite having comparable percolation thresholds, CB/CNTs hybrid conductive network showed better conductivity above the percolation threshold than CB/TPU fibers (Figure S2).…”

A 3D-printed neuromorphic humanoid hand for grasping unknown objects

“…GF is one of the representative parameters to define the sensitivity of sensors. Therefore, we compared the GF of our origami sensor with that of other sensors as shown in Table 1 ( Garcia et al., 2021 ). Our sensor does not show the best performance among them, but it has potential to be improved because its mechanism relies more on the structure variation than the material property.…”