This paper presents a recent investigation into sensing and measurement of large strain (>10%) based on variation in electrical resistance of reduced graphene oxide. Sensing and measurement of large strain is of great interest for highly flexible and stretchable systems with applications including monitoring human motion, soft robotics and structural health monitoring. Measuring large strains is challenging because many traditional engineering materials yield or fracture at relatively small strains. Nanoparticle-based strain gauges have been investigated and regularly found to survive strains up to 50% with sensitivity (gauge factor) near 100 times greater than conventional metal foil strain gauges. However, most of these nanoparticle-based strain gauges have been made of toxic materials and are expensive to create (e.g. nickel or silver nanoparticles). Recent research has uncovered incredible properties of graphene which can overcome these issues, composed of inexpensive and natural carbon and surviving strain up to 20%. Direct synthesis and deposition of graphene monolayers remains expensive and complex. Reduced graphene oxide shares many of the phenomenal properties of graphene but can be synthesized with simple drop casting and thermal reduction techniques.

This paper presents recent research into measurement of strains greater than 10% by monitoring the change in resistance of reduced graphene oxide patches. A combination of electrical, mechanical, and microscopic investigations were performed to understand the electromechanical behavior of the material. A highly linear relation between resistance and strain was observed at relatively small strains, consistent with piezoresistive theory from monolithic materials, while a nonlinear relation was observed at strains from 12% to 20%, consistent with percolative behavior of nanoparticle-based strain sensors. The relation between non-aligned strain and resistance measurement was also investigated (e.g. perpendicular and off axis). Continuing investigation is seeking to understand the origins of the electrical and mechanical behavior observed.

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