Highly adaptive and broadband triboelectric energy harvester with stretching silicone rubber strip for variable harmonic frequency vibration

Du, Taili; Shen, Dianlong; Xi, Ziyue; Yu, Hongyong; Dong, Fangyang; Zhao, Cong; Zhu, Meixian; Zou, Yongjiu; Sun, Peiting; Xu, Minyi

doi:10.1007/s12274-023-6309-3

Cited by 1 publication

(1 citation statement)

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“…As science and technology progress, the TENG's role in signal sensing via vibration has expanded into speech recognition, prompting the need for improved material structure, response speed, sensitivity, noise immunity, and deployment contexts. Notably, its capacity to detect signals through vibration has significantly contributed to advancements in acoustic science [27][28][29][30][31][32][33][34][35][36][37]. For instance, Zheng's team engineered a TENG capable of recognizing road and traffic sounds and deployed it on roads or buildings to differentiate between passing vehicles and structural flaws [38].…”

Section: Introductionmentioning

confidence: 99%

Flexible Self-Powered Low-Decibel Voice Recognition Mask

Li,

Shi,

Chen

et al. 2024

Sensors

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In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.

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