Kunming University

Developing flexible electronic materials that integrate high strength, electrical insulation, and reliable sensing remains challenging. This is because the void structures inherent in nanocomposite films significantly compromise their mechanical performance and functionality. This study systematically investigates the influence of calcium sulfate crystallite (CSC) dimensions and content on the void structure, mechanical properties, and functional characteristics of aramid nanofiber (ANF) composite films via the incorporation of CSCs into the ANF matrix. The underlying mechanisms governing these effects were thoroughly examined. CSCs with different dimensions were first synthesized via a hydrothermal method. Then, ANF/CSC composite films were fabricated through a sequential process of vacuum-assisted filtration followed by hot-pressing. The results indicate that the composite film incorporating medium-sized CSCs at 30 wt% exhibits the optimal reinforcement effect, achieving a tensile strength of 177.7 MPa, which represents a 289% improvement compared to the pristine ANF film. It was found that the CSCs form a three-dimensional network architecture within the ANF matrix, which effectively fills the voids and enhances the degree of orientation. Furthermore, a Janus-structured ANF/CSC-ANF/AgNW film was constructed, achieving the integration of electrical insulation on one side and conductivity on the other. The sample containing 30 wt% AgNWs exhibited rapid Joule heating, reaching 110 °C within 10 seconds under an applied voltage of 10 V. Moreover, it maintained stable electrical performance even after 750 bending cycles in sensing tests. This study provides a strategy to effectively enhance the mechanical and insulating properties of composite materials through a three-dimensional network of CSCs and ANFs and achieves integrated assembly of an insulating substrate and a conductive sensing layer by introducing a Janus structure. It offers greater possibilities for the application of high-performance flexible electronic devices in extreme environments.

Traditional physiological monitoring methods in basketball games are often invasive and struggle to capture long-term fluctuations in movement quality. This study presents a framework for assessing movement quality degradation based on video-based virtual sensing and an Adaptive Graph Convolution Network (AGCN)-Mamba network. First, the framework extracts human skeleton sequences via pose estimation and transforms them into dynamic feature tensors resembling Inertial Measurement Unit (IMU) data, thereby simulating the data stream of wearable devices. The AGCN module then captures coordination relationships between non-adjacent limbs, while Mamba’s selective scanning strategy enables long-term fatigue tracking throughout a game with low computational complexity. Experimental results under a subject-disjoint protocol demonstrated a Top-1 action recognition accuracy of 94.82%, and the movement quality scores correlated strongly with expert evaluations (correlation coefficient r = 0.91). A per-frame latency of 12.08 ms further indicates the framework’s applicability in non-invasive movement monitoring. These results show that combining adaptive graph learning with linear state-space modeling allows effective extraction of deep features and efficient edge computation. The proposed method provides a solid theoretical and technical foundation for the development of next-generation, low-power, and high-precision intelligent wearable fitness monitoring systems.