From network to channel: Crack-based strain sensors with high sensitivity, stretchability, and linearity via strain engineering

Abstract

High-performance stretchable strain sensors are highly desirable for various scenarios, such as health monitoring and human-robot interfaces. Here, we propose a universal strain engineering strategy that introduces an inhomogeneous spatial distribution of stress and promotes crack propagation behavior leading to a critical state between network and channel morphologies, achieving stretchable strain sensors with high sensitivity, a wide working range and good linearity. Approaches for introducing soft-rigid interfaces, enlarging elastic modulus mismatches and matching dimensions have been employed to execute the strategy for network-crack strain sensors with collapsed nanocone cluster structures as representatives. The strain sensors can be tuned to realize a gauge factor of 690.95 in a linear working range of 0–40% (R2 = 0.993) or a gauge factor of 113.70 in a larger linear working range of 0–120% (R2 = 0.999). Intraocular pressure monitoring and dynamic facial asymmetry assessment have been demonstrated based on these sensors to show their great application potential.