Self-Powered Flexible Triboelectric Nanogenerators for Real-Time, Adaptive Gesture-Based Emergency Communication

The triboelectric nanogenerator (TENG) represents a distinctive energy harvesting technology. However, polymer-based TENG faces substantial challenges in retaining stable triboelectric output and structural robustness across temperature extremes and humidity gradients. These limitations stem from inherent material degradation and interfacial charge dissipation under harsh conditions. This study presents a TENG composed of an aromatic polyamide nanofiber membrane doped with carbon nanotubes and barium titanate (CB-PMIA), and a reduced graphene oxide-doped nanomembrane of poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate) (rGO-PP). The resulting CB-PMIA/rGO-PP nanocomposite offers excellent thermal stability, mechanical stability, thermal and moisture comfort, and a tunable triboelectric output performance. Under standardized testing conditions (2 Hz frequency, 35 N applied pressure), the CB-PMIA/rGO-PP TENG demonstrated an open-circuit voltage of 158 V and a short-circuit current of 1.6 μA. Concurrently, it generated a maximum peak power density of 1.2 W m^-2 under a 20 MΩ resistive load, highlighting its superior energy conversion efficiency in practical operating scenarios. Importantly, the TENG maintains stable output performance across a wide temperature range (-30 to 200 °C) and humidity levels (relative humidity of 45 to 95%), even sustaining 84.9% and 76% of output at 200 and -30 °C, respectively. This research introduces an innovative framework for sustainable energy harvesting in extreme operational environments. Through optimized interfacial charge transfer efficiency, the developed device seamlessly integrates with low-power Bluetooth Low Energy sensing modules, enabling real-time wireless sensory data acquisition for human motion analysis. The system supports both gesture-recognition-based hierarchical alerting mechanisms and multimodal safety monitoring, demonstrating robust environmental adaptability and signal stability under simulated harsh conditions. This advancement paves the way for the development of advanced human-machine collaborative safety systems capable of operating in extreme temperature and humidity environments.