Hierarchically Piezoelectric Aerogels for Efficient Sound Absorption and Machine‐Learning‐Assisted Sensing

Abstract Ubiquitous noise pollution has been associated with significant negative impacts on human health. However, current porous sound‐absorbing materials encounter considerable obstacles such as thick density, narrow absorbing band, and limited function. Here, a facile‐producing method for lightweight and efficiently sound‐absorbing aerogels made from bacterial cellulose (BC) and poly(vinyl alcohol) (PVA) is presented. The fabricated anisotropic aerogels with directional pores exhibit a minimum density as low as 11.3 mg cm −3 . Meanwhile, the lamellar aerogels with low areal density (20.89 mg cm −2 ) exhibit remarkable noise attenuation performance with the noise reduction coefficient of 0.51.Furthermore, the BC‐PVA‐Ba 0.85 Ca 0.15 Zr 0.9 Ti 0.1 O 3 (BCZT) aerogels show enhanced sound absorption performance, and these aerogels are self‐powered sensors to monitor vehicle collisions and human gestures. The algorithm yields high accuracy in human gesture recognition (100%) based on the deep‐learning model. These aerogels offer an encouraging application prospect in the automobile field to realize car weight reduction and vehicle's intelligent control system.