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Junrui Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Core-Sheath Structured Yarn for Biomechanical Sensing in Health Monitoring

    by Wenjing Fan, Cheng Li, Bingping Yu, Te Liang, Junrui Li, Dapeng Wei, Keyu Meng

    The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems.