Although ferroelectric composites have been reported to enhance the performance of triboelectric (TE) devices, their performances are still limited owing to randomly dispersed particles. Herein, we introduce high-performance TE sensors (TESs) based on ferroelectric multilayer nanocomposites with alternating poly­(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE) and BaTiO3 (BTO) nanoparticle (NP) layers. The multilayers comprising alternating soft/hard layers can induce stress concentration and increase the effective stress-induced polarization and interfacial polarization between organic and inorganic materials, leading to a dielectric constant (17.06) that is higher than those of pure PVDF-TrFE films (13.9) and single PVDF-TrFE/BTO nanocomposites (15.9) at 10 kHz. As a result, the multilayered TESs with alternating BTO NP layers exhibit TE currents increased by 2.3 and 1.5 times compared to pure PVDF-TrFE without BTO NPs and PVDF-TrFE/BTO nanocomposites without multilayer structures, respectively. The multilayered TESs exhibit a high pressure sensitivity of 0.94 V/kPa (48.7 nA/kPa) and output power density of 29.4 μWcm–2, enabling their application in the fabrication of highly sensitive healthcare monitoring devices and high-performance acoustic sensors. The suggested architecture of ferroelectric multilayer nanocomposites provides a robust platform for TE devices and self-powered wearable electronics.

Textile-based sensors can perceive and respond to environmental stimuli in daily life, and hence are critical components of wearable devices.

Accurate transmission of biosignals without interference of surrounding noises is a key factor for the realization of human-machine interfaces (HMIs). We propose frequency-selective acoustic and haptic sensors for dual-mode HMIs based on triboelectric sensors with hierarchical macrodome/micropore/nanoparticle structure of ferroelectric composites. Our sensor shows a high sensitivity and linearity under a wide range of dynamic pressures and resonance frequency, which enables high acoustic frequency selectivity in a wide frequency range (145 to 9000 Hz), thus rendering noise-independent voice recognition possible. Our frequency-selective multichannel acoustic sensor array combined with an artificial neural network demonstrates over 95% accurate voice recognition for different frequency noises ranging from 100 to 8000 Hz. We demonstrate that our dual-mode sensor with linear response and frequency selectivity over a wide range of dynamic pressures facilitates the differentiation of surface texture and control of an avatar robot using both acoustic and mechanical inputs without interference from surrounding noise.

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