A Selective-Response Hypersensitive Bio-Inspired Strain Sensor Enabled by Hysteresis Effect and Parallel Through-Slits Structures

Highlights A bio-inspired flexible strain sensor with hypersensitivity and highly selective frequency response is prepared by styrene–isoprene–styrene combined with monolayer graphene. Benefiting from the structural design inspired by nature and hysteresis of viscoelastic materials, bio-inspired structures, and original materials' properties complement each other. The frequency recognition resolution of bio-inspired flexible strain sensor reaches 0.2 Hz, making it ideal for human–computer interaction and mechanical equipment health inspection. Flexible strain sensors are promising in sensing minuscule mechanical signals, and thereby widely used in various advanced fields. However, the effective integration of hypersensitivity and highly selective response into one flexible strain sensor remains a huge challenge. Herein, inspired by the hysteresis strategy of the scorpion slit receptor, a bio-inspired flexible strain sensor (BFSS) with parallel through-slit arrays is designed and fabricated. Specifically, BFSS consists of conductive monolayer graphene and viscoelastic styrene–isoprene–styrene block copolymer. Under the synergistic effect of the bio-inspired slit structures and flexible viscoelastic materials, BFSS can achieve both hypersensitivity and highly selective frequency response. Remarkably, the BFSS exhibits a high gage factor of 657.36, and a precise identification of vibration frequencies at a resolution of 0.2 Hz through undergoing different morphological changes to high-frequency vibration and low-frequency vibration. Moreover, the BFSS possesses a wide frequency detection range (103 Hz) and stable durability (1000 cycles). It can sense and recognize vibration signals with different characteristics, including the frequency, amplitude, and waveform. This work, which turns the hysteresis effect into a "treasure," can provide new design ideas for sensors for potential applications including human–computer interaction and health monitoring of mechanical equipment.