AI-Assisted Design and Multiphysics Optimization of Graphene-Enhanced Triboelectric Nanogenerators for Biomedical Energy Harvesting

Abstract

Triboelectric nanogenerators (TENGs) have emerged as promising self-powered energy sources for biomedical applications, particularly for wearable and implantable devices requiring flexible, lightweight, and sustainable power generation. This study presents the design and multiphysics simulation of a flexible graphene-enhanced TENG based on polydimethylsiloxane (PDMS) integrated with layered graphene to improve charge transfer, electrical conductivity, and mechanical durability. The influence of material properties, dielectric behavior, electrostatic potential distribution, and mechanical deformation on TENG performance is systematically investigated using ANSYS-based multiphysics simulations. Fundamental triboelectric mechanisms, including contact electrification, electrostatic induction, charge transfer, and contact–separation dynamics, are incorporated into the analysis to evaluate the electrical response of the proposed device. The optimized graphene-enhanced PDMS TENG achieves a peak output voltage of 180 V, a short-circuit current of 6 μA, and a maximum power output of 0.73 mW at a load resistance of 10⁷ Ω. The simulation results demonstrate that graphene incorporation enhances charge retention and electrical output while maintaining the flexibility and mechanical robustness of the PDMS-based structure. Furthermore, structural and material optimization significantly influences the electrostatic response and energy-conversion characteristics of the TENG. The proposed graphene-enhanced TENG demonstrates considerable potential for self-powered biomedical systems, including wearable physiological monitoring, rehabilitation devices, low-power healthcare sensors, and smart medical implants, thereby contributing to the development of sustainable energy solutions with reduced dependence on conventional batteries.