Chemically functionalized cellulose triboelectret nanogenerator for machine-learning-enabled tactile sensing

Abstract

Self-powered energy harvesting technology has attracted significant attention for use in biomedical devices, smart sensors, wearables and implantable electronics. Despite the high efficiency and material versatility of triboelectric nanogenerators, long-term stability issues still persist due to poor charge retention abilities. In this study, a new type of nanogenerator is introduced, which simultaneously employs both triboelectric and electret features, and is referred to as a triboelectret nanogenerator (E-TENG). Most importantly, both triboelectric active layers of an E-TENG are composed of cellulosic materials that effectively overcome the long-standing issue of charge annihilation in a TENG. To tune the surface potential, cellulose nanofibers have been functionalised with nitro groups to enable electron-withdrawing abilities that could provide a tribo-negative surface, whereas electron-donating properties are introduced using a stearoyl group for a tribo-positive surface. The hydrophobic electret functionality, which enhances charge retention and long-term stability, is achieved by forming an aerogel structure in the tribo-positive counterpart. The E-TENG outperforms a traditional cellulose-based TENG with a maximum power density of 6.8 W m⁻², with a stable electrical output confirmed by long-term durability testing over 90 days. To demonstrate real-time sensing abilities, the E-TENG is employed to monitor different biomechanical signals and for tactile sensing. Additionally, machine learning analysis achieves 98.6% overall accuracy in predicting finger touch, suggesting numerous applications in gesture recognition, human–machine interfacing, robotics, healthcare and consumer electronics. These findings pave the way for scalable, natural biopolymer-based electronics, enabling next-generation wearable devices, human–machine interfaces, and pervasive healthcare diagnostics.