Based on the super light trapping property of butterfly Trogonoptera brookiana wings, the SiO2 replica of this bionic functional surface was successfully synthesized using a simple and highly effective synthesis method combining a sol–gel process and subsequent selective etching. Firstly, the reflectivity of butterfly wing scales was carefully examined. It was found that the whole reflectance spectroscopy of the butterfly wings showed a lower level (less than 10 %) in the visible spectrum. Thus, it was confirmed that the butterfly wings possessed a super light trapping effect. Afterwards, the morphologies and detailed architectures of the butterfly wing scales were carefully investigated using the ultra-depth three-dimensional (3D) microscope and field emission scanning electronic microscopy (FESEM). It was composed by the parallel ridges and quasi-honeycomb-like structure between them. Based on the biological properties and function above, an exact SiO2 negative replica was fabricated through a synthesis method combining a sol–gel process and subsequent selective etching. At last, the comparative analysis of morphology feature size and the reflectance spectroscopy between the SiO2 negative replica and the flat plate was conducted. It could be concluded that the SiO2 negative replica inherited not only the original super light trapping architectures, but also the super light trapping characteristics of bio-template. This work may open up an avenue for the design and fabrication of super light trapping materials and encourage people to look for more super light trapping architectures in nature.

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.

Sensitivity enhancement for pressure sensors over a broad linear range can improve sensing performance for a wide range of applications such as health monitoring and artificial intelligence. Here, inspired by the high-precision mechanosensory mechanism of the scorpion, a bioinspired piezoresistive pressure sensor (BPPS) is reported for the synergistic enhancement of sensitivity and linearity at 65.56 millivolts per volt per kilopascal and 0.99934, respectively, in a pressure range from 0 to 500 kilopascals. The BPPS can distinguish laminar, transitional, and turbulent flows as well as identify approaching objects of different shapes with an accuracy exceeding 85.42% by integrating a wavelet transform algorithm and the ResNet18 deep learning network. As a proof of concept, BPPS has been engineered in a hexapod robot to enable near-body flow field sensing for active collision avoidance. This work underscores the potential to leverage key design concepts inspired by living insects for improved sensing performance and offers structural insights for other high-precision sensors.