Intelligent and Energy-Autonomous Wearable and Implantable Biosensors: Nanomaterial Interfaces, Energy Harvesting, Edge AI, and Long-Term Reliability

Wearable and implantable biosensors are becoming small distributed biomedical systems rather than isolated transducers. Their practical performance depends on how the sensing interface, analog front end, power source, local computation, wireless link, packaging, and therapeutic output interact over time. The analysis focuses on that cross-layer problem, with emphasis on nanomaterial interfaces, energy autonomy, edge intelligence, and long-term reliability. Graphene, carbon nanotubes, MXenes, and transition-metal dichalcogenides are discussed across electrochemical, field-effect, impedance, optical, and radio-frequency transduction. Mechanical nanogenerators, biofuel cells, wireless power transfer, and hybrid storage are compared using the energy actually available after rectification and regulation rather than peak generator output alone. Quantitative re-plots illustrate non-monotonic carbon-nanotube loading in triboelectric layers and voltage-tunable few-layer-graphene microwave components. Edge AI is treated as part of the power and measurement architecture: local inference can reduce radio traffic and latency, but introduces model drift, uncertainty, and update requirements. The final sections connect biofouling, encapsulation, mechanical fatigue, calibration drift, wireless safety, and algorithm lifecycle to a common validation ladder for wearable, insertable, and implantable systems.