Strain Gauges Based on CVD Graphene Layers and Exfoliated Graphene Nanoplatelets with Enhanced Reproducibility and Scalability for Large Quantities

The two-dimensional material graphene promises a broad variety of sensing activities. Based on its low weight and high versatility, the sensor density can significantly be increased on a structure, which can improve reliability and reduce fluctuation in damage detection strategies such as structural health monitoring (SHM). Moreover; it initializes the basis of structure–sensor fusion towards self-sensing structures. Strain gauges are extensively used sensors in scientific and industrial applications. In this work, sensing in small strain fields (from −0.1% up to 0.1%) with regard to structural dynamics of a mechanical structure is presented with sensitivities comparable to bulk materials by measuring the inherent piezoresistive effect of graphene grown by chemical vapor deposition (CVD) with a very high aspect ratio of approximately 4.86 × 10(8). It is demonstrated that the increasing number of graphene layers with CVD graphene plays a key role in reproducible strain gauge application since defects of individual layers may become less important in the current path. This may lead to a more stable response and, thus, resulting in a lower scattering.. Further results demonstrate the piezoresistive effect in a network consisting of liquid exfoliated graphene nanoplatelets (GNP), which result in even higher strain sensitivity and reproducibility. A model-assisted approach provides the main parameters to find an optimum of sensitivity and reproducibility of GNP films. The fabricated GNP strain gauges show a minimal deviation in PRE effect with a GF of approximately 5.6 and predict a linear electromechanical behaviour up to 1% strain. Spray deposition is used to develop a low-cost and scalable manufacturing process for GNP strain gauges. In this context, the challenge of reproducible and reliable manufacturing and operating must be overcome. The developed sensors exhibit strain gauges by considering the significant importance of reproducible sensor performances and open the path for graphene strain gauges for potential usages in science and industry.