Degradation Mechanisms and Intelligent Recognition of PTFE Triboelectric Performance under Hygrothermal Cycling Environment.

In this study, we systematically investigate the triboelectric performance degradation of polytetrafluoroethylene (PTFE)-based triboelectric nanogenerators (TENGs) under high-low temperature cycling and humidity variations, a critical yet underexplored challenge in extreme environmental applications. Under accelerated aging conditions spanning 0-300 thermal cycles and 0-90% RH, PTFE films demonstrated progressively worsening electrical output, their short-circuit current declining steadily from 7.2 to 4.6 μA, alongside an open-circuit voltage drop from 400 to 240 V. Surface characterization via scanning electron microscopy, atomic force microscopy, and X-ray photoelectron spectroscopy reveals that thermal cycling induces microstructural defects and chemical oxidation, where oxygen content replaced C-F bonds with weaker electron-accepting groups (C-O, C═O). This oxidative degradation reduced PTFE's electron affinity and directly impaired charge transfer efficiency in TENGs. To address performance unpredictability, an improved extreme learning machine algorithm is developed, achieving 100% accuracy in real-time classification of PTFE aging states by analyzing triboelectric signals. Furthermore, paired with pristine PTFE, aged PTFE modified surface states enable a 21.8-fold power output enhancement and outperform conventional configurations. These findings establish a mechanistic framework linking thermal-humidity aging to triboelectric decay while proposing machine-learning-driven predictive maintenance strategies for TENGs in aerospace, energy systems, and autonomous devices operating in harsh environments.