Theoretical investigation and experimental verification of the self-powered acceleration sensor based on triboelectric nanogenerators (TENGs)

Self-powered acceleration sensors based on triboelectric nanogenerators (TENGs) are critical components for vibration detection, which have promising applications in Internet of Things, wearable electronics and sensor networks. However, the development of this sensor is restricted by lack of the working mechanism. Herein, we propose a novel theoretical model V-Q-a based on contact-separation TENGs to systematically illuminate the mechanism of the self-powered acceleration sensor under different motion. Based on the model, the self-powered sensor’s output performances such as transferred charges, output voltage and sensitivity are deeply analyzed under common uniform accelerated and sinusoidal motions. Also, a self-powered acceleration sensor with silk-fibroin triboelectric layer is fabricated to verify the V-Q-a model. The test results show that experimental verification data agrees well with the theoretical analysis. It also exhibits a high sensitivity of 19.07 V s2 m−1 of the as-fabricated sensor with higher stability, which is 12 times as higher than the self-powered accelerometer fabricated by Zhang et al. with sensitivity of 15.56 V g−1. The excellent output performance enables the acceleration sensor to be applied in wearable devices (passometer) and machine vibration monitoring. This work advances an in-depth understanding of self-powered acceleration sensor based on TENGs, which will effectively guide for optimizing sensor structure and performance in future specific applications. •A novel V-Q-a theoretical model for a self-powered acceleration sensor based on TENGs under uniform accelerated and sinusoidal motions.•Developed a self-powered and high sensitivity acceleration sensor based on the V-Q-a model.•The self-powered acceleration sensor exhibits a high sensitivity of 19.07 V/(m/s2).•Application prospects including wearable devices (passometer) and machine vibration monitoring.