Fingerprint-Mimetic Fibrous Glove via Shear-Induced Alignment and Dynamic Compensation for Multimodal Tactile Sensing.

Flexible devices are key for human-machine interaction in smart homes and virtual reality. However, existing wearable devices are limited by single-mode sensing, irreversible conductive network damage under deformation, and a lack of tactile enhancement, hindering high sensitivity and multimodal perception. Herein, inspired by the arc-shaped topology of human fingerprints, we propose a fiber-based multimodal tactile smart glove (silver/ionic liquid/TPU glove, named SIT Glove). Using Jeffery orbit dynamics, shear flow during wet-spinning aligned silver nanoflakes (AgNFs) axially, while an ionic liquid served as a dynamic conductive compensation medium, yielding SIT fibers with an "electronic network + ionic polarization" synergy. The fibers exhibit excellent strain, pressure, and temperature sensing properties: conductivity 1.66 × 104 S/m, elongation at break 387.96%, gauge factor 9826.244 (80-100% strain), pressure sensitivity 4.4340 kPa-1 (50-60 kPa), and TCR -0.0453 °C-1 (30-40 °C). Meanwhile, embroidering SIT fibers onto glove fingertips in fingerprint patterns created an 18-channel tactile array. Furthermore, with machine learning for gesture recognition and deep learning (1D-CNN) for fabric classification, the glove achieved 93.98% accuracy for 6 gestures and 86.84% for 4 fabrics and demonstrated body temperature monitoring, hot/cold sensing, virtual game interaction, and fan control. This work offers a bioinspired paradigm for wearable devices with high sensitivity, a wide response range, and multifunctional integration.