Research Achievements
Publications
Research Achievements
Publications
Where Jeju’s nature meets technology shaping
a sustainable future.
Jeju National University
Green Hydrogen Glocal Leading Research Center
TEL. 064-754-4446 ｜ E-MAIL. gh2rlrc@gmail.com
LOCATION. D208, Engineering Building 4, 102
Jejudaehak-ro, Jeju-si, Jeju Special Self-Governing Province, Jeju National University (Arail-dong)
Copyright ⓒ Jeju National University Green Hydrogen Glocal Leading Research Center. All right reserved.
 | Jeju National University Green Hydrogen Glocal Leading Research Center TEL. 064-754-4446 ｜ E-MAIL. gh2rlrc@gmail.com LOCATION. D208, Engineering Building 4, 102 Jejudaehak-ro, Jeju-si, Jeju Special Self-Governing Province, Jeju National University (Arail-dong), Republic of Korea |
Where Jeju’s nature meets technology shaping
a sustainable future.
Copyright ⓒ Jeju National University Green Hydrogen Glocal Leading Research Center. All right reserved.
Abstract
Multilayer structured piezo-triboelectric hybrid nanogenerators (m-PT-HNG) are emerging as promising candidates for nextgeneration 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 singlelayer PCNG. The ferroelectric, piezoelectric performance of Cu2O-doped 0.3Ba0.7Ca0.3TiO3-0.7BaSn0.12Ti0.88O3 (BCST0.01Cu2O) 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