Triboelectricity-driven intelligent self-powered healthcare: From fundamental bio-Interface mechanisms to system integration

초록

Wearable and biomedical electronics are shifting from intermittent, user-activated devices toward continuously operating, context-aware systems for long-term monitoring and therapy. This paradigm shift exposes fundamental limitations of conventional battery-centered architectures in terms of bulk, rigidity, maintenance burden, and compatibility with chronic skin/tissue integration. Triboelectric nanogenerators (TENGs), which convert low-frequency mechanical stimuli into electrical outputs through triboelectrification and electrostatic induction, have emerged as a compelling platform for intelligent self-powered healthcare because they can harvest biomechanical energy while simultaneously generating information-rich sensing signals in soft, conformal, and biointerface-compatible formats. This review presents a multi-scale and translational perspective on triboelectricity-based intelligent self-powered healthcare, spanning from bio-interfaced fundamental mechanisms to system integration. First, the operating physics of TENGs are analyzed from the viewpoint of mechanically driven electromechanical transduction. Key design strategies for wearable platforms are also discussed, including mode selection, biointerface engineering, skin compatibility, and strategies to enhance performance under low-frequency, irregular human motion through materials modulation and mechanical/kinematic design. On top of that, wearable healthcare applications in which triboelectric systems function as self-powered sensors and/or energy harvesting modules are discussed. The application scope covers across three major directions: human-machine interfaces and rehabilitation assistance, autonomous wireless monitoring and multimodal biosignal sensing, and wearable therapeutic platforms. Then, the implantable and bio-integrated triboelectric systems are discussed with classification of them according to the origin of biomechanical excitation-voluntary musculoskeletal motion versus autonomic visceral organ motion. Furthermore, this review examines system-level opportunities and challenges. These include power management for intermittent outputs and dual-signal utilization for concurrent energy extraction and sensing. Machine-learning-based analytics for nonlinear triboelectric waveforms are also addressed, along with translational barriers related to regulation, standardization, ethics, and long-term reliability. By connecting device physics, biointerface design, application architectures, and clinical translation requirements, this review delineates the critical pathways toward deployable TENG-enabled biointerface systems for personalized, continuous, and minimally invasive intelligent healthcare.

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