A biowaste material-based low-cost environment-friendly triboelectric nanogenerator for self-powered sensing application

Abstract

Global issues are addressed mainly by sustainable energy harvesting technology to maintain the social ecology. There is a lot of interest in creating flexible triboelectric nanogenerators (TENGs) using inexpensive, non-toxic natural materials, owing to a simple and cost-effective process. Utilizing a variety of waste materials, this technology is incredibly effective at transforming untidy environmental energies into green electricity for a range of ingenious applications. Herein, we have developed a novel water hyacinth root (WHR)-based green triboelectric nanogenerator (TENG) for self-powered sensing applications. For the construction of the TENG, WHR is affixed to fabrics to function as one triboelectric layer, while human skin serves as the opposing triboelectric layer. Water hyacinth roots are abundantly accessible and inexpensive, decreasing TENG production costs and providing an ecologically friendly and cost-free alternative to more expensive materials. Furthermore, the water hyacinth root is crucial for promoting environmentally friendly development, as it converts bio waste into a sustainable energy source, thereby creating an economy that prioritizes sustainability. WHRs exhibit fine fibers, sufficient tensile strength & flexibility, a rich composition of cellulose and lignin, rough texture, and abundant functional groups, which play a key role in demonstrating the electron-accepting ability of the WHRs. Based on the potential of this device, we have a power density of 5 W m⁻² and a sensitivity of 3.2 V kPa⁻¹. The electrical output was analyzed under various mechanical stimuli, proving its durability and reliability in energy harvesting. The current study anticipated employing natural waste to create healthcare monitoring, tactile sensing, and energy-harvesting devices, as well as potential uses in self-powered sensors for various security applications, Internet of Things (IoT), and human–machine interfaces. The WHR-TENG-based self-powered sensor exhibited an identification accuracy of 99.3% using a deep learning algorithm. Therefore, this work proves the need to develop waste-material-based TENGs for environmentally-friendly and economical self-powered devices.