Quantifying the Piezoresistive Mechanism in High Performance Flexible Printed Graphene Strain Sensors

Printed strain sensors will be important in applications such as wearable sensors which monitor breathing and heart function. Such sensors need to combine high sensitivity and low resistance with other factors such as cyclability, low hysteresis and minimal frequency/strain-rate dependence. Although nanocomposite sensors can display high G, they often perform poorly in the other areas. Recently, evidence has been growing that printed, polymer-free networks of nanoparticles, such as graphene nanosheets, display very good all round sensing performance, however, the details of the sensing mechanism are currently poorly understood.<br/>Here we perform a detailed characterisation of the thickness dependence of piezoresistive sensors based on printed networks of graphene nanosheets. We find both conductivity and gauge factor to display percolative behaviour at low network thickness but bulk-like behaviour for networks above ∼100 nm thick. Using percolation theory, we derived an equation for gauge factor as a function of network thickness which well-described the observed thickness dependence including the divergence in gauge factor as the percolation threshold is approached. Our analysis shows that the dominant contributor to the sensor performance is not the effect of stain on inter-nanosheet junctions but the strain-induced modification of network structure. Finally, our results show that these networks display excellent cyclability, hysteresis and frequency/strain-rate dependence and gauge factors as high as 350.