Capacitive Structure-Based Acoustic Triboelectric Nanogenerator for Advanced Warning Sound Recognition

Abstract

Sound is a powerful communication tool that outperforms visual signals in emergencies. However, research on sound energy harvesting has been limited because of its relative weakness as an ambient energy source. The development of sensitive and efficient energy harvesting technologies is crucial for human safety applications. Triboelectric nanogenerators (TENGs) are energy-harvesting devices that generate electrical signals through mechanical movement. However, previous studies have used conventional vertical contact separation mode structures, which rely solely on the membrane surface charge. A breakthrough is needed to increase their sensitivity and ensure long-term usability. Herein, a capacitive-structure-based noncontact acoustic TENG (CS-TENG) is developed. Electrical analysis demonstrates high sensitivity and improved voltage and current. CS-TENG outperforms conventional A-TENGs at 90 dB, achieving maximum peak voltages and currents of over 59.74 and 85.1%, respectively. CS-TENG accurately detects human voices, frequency warning sounds, and reality warning sounds with 95, 99, and 100% accuracy, making it suitable for industries with limited visual information.