Publication

NMSL Lab

Publication

Multilayered piezo-tribo hybrid nanogenerator integrated with machine learning for advanced sign language to speech system
Author
Monunith Anithkumar, Asokan Poorani Sathya Prasanna, Nagamalleswara Rao Alluri, Thanjan Shaji Bincy, Kwi‑Il Park, Sang‑Jae Kim
Journal
Advanced Composites and Hybrid Materials
Status
Volume 8, Issue 4
Page
306
Year
2025

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Abstract

Multilayer structured piezo-triboelectric hybrid nanogenerators (m-PT-HNG) are emerging as promising candidates for next-generation wearable sensors owing to their ability to harvest energy with high sensitivity and enhanced output. In this work, we report a reliable and sensitive multilayered intrinsic piezo-tribo hybrid nanogenerator (m-PT-HNG) based on a multilayer piezoelectric composite nanogenerator (m-PCNG) architecture combined with triboelectric functionality. The m-PCNG fabricated via parallelly connected multilayers demonstrate significant enhancement of output performance compared to single-layer PCNG. The ferroelectric, piezoelectric performance of Cu2O-doped 0.3Ba0.7Ca0.3TiO3-0.7BaSn0.12Ti0.88O3 (BCST-0.01Cu2O) ceramic fillers was systematically optimized by applying various piston loads (10 to 50 kN) and an electric field of 25 kV/cm. The resulting intrinsically coupled m-PT-HNG produces an instantaneous power density of 85.36 mW/m2 at 200 MΩ. To demonstrate practical utility, a sign language recognition smart glove (SLR-SG) was developed integrating the five m-PT-HNGs, enabling accurate sign language classification through a machine learning algorithm and real-time sign language to speech conversion via a mobile application.