Self-Powered Microsystem for Ultra-Fast Crash Detection via Prestressed Triboelectric Sensing - Yiqun Wang; Yuhan Wang; Xinzhi Liu; Xiaofeng Wang; Keren Dai; Zheng You (Research)
Reliable detection of high-g shocks in extreme impact scenarios, such as automobile collisions, is essential for ensuring occupant safety. Conventional shock sensors based on piezoresistive or capacitive mechanisms often underperform in high-g environments due to their structural complexity, resulting in delayed or missed detection. Here, we present a self-powered high-g shock sensor that combines a triboelectric transducer with a prestressed structure to deliver large signal amplitude and minimal oscillation. The prestress mechanism enhances initial contact strength, achieving a 400% increase in signal amplitude and reduced oscillation. We further developed a self-powered, compact (<4.5 cm3) microsystem that integrates the shock sensor, a signal processing module, airbag triggering circuitry, and a high-g-resistant supercapacitor as a backup power source. The microsystem achieves ultra-fast shock detection and airbag activation with a delay of less than 0.2 ms. Furthermore, its power demand is 80% lower than that of commercial high-g sensors, while the pre-charged supercapacitor ensures operational stability. To further extend the functionality of the device, we designed a lightweight collision target classification algorithm using ensemble learning and feature importance analysis, which could accurately distinguish between automotive collisions with hard, brittle, and soft materials. This study advances triboelectric nanogenerators for high-g shock sensing, offering improved reliability, performance, and real-world adaptability.