Sodium-Alginate Composite Nanofiber-Based Triboelectric Sensor for Self-Powered Wrist Posture Identification

Based on the rapid development of the Internet of Things and the demand for self-powered sensors, triboelectric nanogenerators can provide an efficient and environmentally friendly solution. Therefore, developing high-performance and sustainable material-based TENGs is a recent key point to achieve low-carbon society. Herein, we explore polyacrylonitrile (PAN)/sodium-alginate composite nanofiber films as an enhancement tribo-positive material. The PAN/sodium-alginate triboelectric nanogenerator (PS-TENG) assembled from this tribo-positive material is boosted several times to a pure PAN nanofiber film. In addition, the PS-TENG exhibits an excellent signal-to-noise ratio of 66.384 dB, fast response time of 7 ms, and high sensitivity of 22.655 +/- 0.78 V/kPa, which shows significant improvement over previous studies using similar materials or configurations. Therefore, we have developed an intelligent sensing system by using an array of four triboelectric sensors and the signal acquisition device of an STM32. Under the combination of K-means and SVM machine learning, the average accuracy of recognizing 12 wrist postures can reach 95.18%. Our study not only introduces an innovative approach for creating highly sensitive triboelectric sensors but also provides a significant way to design a self-powered sensing unit in the Internet of Things.