A Self-Powered, Highly Sensitive, and Frequency-Tunable Triboelectric Acoustic Sensor Inspired by the Human Cochlea

Abstract

Conventional acoustic sensors used in human-machine interfaces often face challenges such as power supply requirements, limited sensitivity, and inability to tune their frequency response. A self-powered, highly sensitive, and frequency-tunable triboelectric acoustic sensor inspired by the human cochlea is introduced. By mimicking hair cells in the organ of Corti, a tapered microhair-structured ferroelectric poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and barium titanate nanoparticle (BTNP) composite film is proposed, which demonstrates a 16-fold increase in triboelectric output voltage (1.3 V) compared to a planar one at 2.8 Pa. Furthermore, inspired by the frequency selectivity of the basilar membrane with gradient structural variations, integrating a mass-beam diaphragm is proposed with varying kirigami length and circular mass diameter that enables precise tuning of the resonance frequency of the sensor, resulting in a 32 times improvement in sensitivity (860 mV Pa-1) compared to a nonbiomimetic sensor (28 mV Pa-1) and an expanded dynamic range. The proposed sensor differentiates between human voices with different frequencies. A robotic hand integrated with the sensor responds to acoustic stimuli with programmed hand gestures, which highlights its potential in acoustic human-machine interfaces. The biomimetic approach to developing a self-powered, highly sensitive, and frequency-tunable acoustic sensor offers new possibilities for intuitive and immersive human-machine interfaces. Herein, a self-powered, highly sensitive, and frequency-tunable triboelectric acoustic sensor is presented. By mimicking the human cochlea structure, we propose a unique micro hair-structured film integrated with a kirigami-patterned diaphragm, which significantly enhances the sensitivity and enables the precise tuning of the resonance frequency of the acoustic sensor. The sensor opens up new possibilities for more intuitive and immersive human-machine interfaces. image