Hexagonal boron nitride composite film based triboelectric nanogenerator for energy harvesting and machine learning assisted handwriting recognition

• Published in: Nano energy Vol. 136; p. 110689
• Main Authors: Umapathi, Reddicherla, Rethinasabapathy, Muruganantham, Kakani, Vijay, Kim, Hanseung, Park, Yonghyeon, Kim, Hyung Kyo, Rani, Gokana Mohana, Kim, Hakil, Huh, Yun Suk
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2025
• Subjects: Handwriting sensing and recognition; Hexagonal boron nitride; Machine learning; Polydimethylsiloxane; Triboelectric nanogenerator; Triboelectric nanogenerator; Hexagonal boron nitride; Handwriting sensing and recognition; Polydimethylsiloxane; Machine learning
• ISSN: 2211-2855
• DOI: 10.1016/j.nanoen.2025.110689

Triboelectric nanogenerators (TENGs) are mechanical energy harvesting systems with unique characteristics. Subsequently, TENGs have recently been the subject of pivotal research. Comparatively, handwriting sensing and recognition are vital for fabricating future-generation biometric technologies. However, most current handwriting recognition systems lack machine learning and self-powered sensing capabilities, crucial for developing intelligent systems. Herein, we report on the fabrication of polydimethylsiloxane (PDMS) negative friction film with pore features and doped with 2D hexagonal boron nitride (100 % h-BN) and defective h-BN (50 %) as efficient dielectric material for improving the electrical behavior of TENGs. A simple, scalable, and facile approach has been employed to create pores in the triboelectric film. The TENG exhibited an optimized voltage of 198.6 V and current of 13.5 μA and attained a power density of 7.86 W/m2 at 40 MΩ. Further, creating pores on the composite film increased the surface roughness and energy-harvesting performance of the device. The TENG sensor was applied to recognize the handwriting of letters written in English by three volunteers, and the decision tree and gradient-boosting machine learning algorithms were used. The results suggest that the fabricated TENG demonstrated a substantial power source for powering portable electronics, showing significant application potential in personal handwriting sensing and machine learning assisted handwriting recognition. [Display omitted] •Creation of pores on the PDMS film increased energy harvesting performances.•PDMS@h-BN composite film is stable, flexible, foldable, bendable, and twistable.•Performed Machine learning studies to recognize the handwriting.•TENG sensor efficiently detected the handwriting signals of the volunteers.