A Highly Permeable and Three-Dimensional Integrated Electronic System for Wearable Human–Robot Interaction - Wang, Wenqiang; Luo, Zebang; Yu, Xingge; Yin, Xiaojia; Xiang, Li; Pan, Anlian (Nano-Micro Letters, 2026)
Permeable electronics promise improved physiological comfort, but remain constrained by limited functional integration and poor mechanical robustness. Here, we report a three-dimensional (3D) permeable electronic system that overcomes these challenges by combining electrospun SEBS nanofiber mats, high-resolution liquid metal conductors patterned via thermal imprinting (50 μm), and a strain isolators (SIL) that protects vertical interconnects (VIAs) from stress concentration. This architecture achieves ultrahigh air permeability (> 5.09 mL cm−2 min−1), exceptional stretchability (750% fracture strain), and reliable conductivity maintained through more than 32,500 strain cycles. Leveraging these advances, we have integrated multilayer circuits, strain sensors, and a three-axis accelerometer to achieve a fully integrated, stretchable, permeable wireless real-time gesture recognition glove. The system enables accurate sign language interpretation (98%) and seamless robotic hand control, demonstrating its potential for assistive technologies. By uniting comfort, durability, and high-density integration, this work establishes a versatile platform for next-generation wearable electronics and interactive human–robot interfaces. Highlights: 1 Breathable and Stretchable 3D Electronics Electrospun SEBS nanofiber mats combined with sub-50 μm liquid metal patterning yield ultrahigh permeability (5.09 mL cm−2 min−1, 2520 g m−2 day−1) and mechanical robustness (750% strain), ensuring zero skin irritation after 1 week. 2 Stable Vertical Interconnects Strain isolators (SIL) decouple substrate deformation from vertical interconnects, maintaining conductivity under 750% strain and >32,500 cycles, surpassing conventional multilayer systems (<250% strain). 3 Gesture Recognition Assistive Glove A wireless glove integrating 5 strain sensors, a three-axis accelerometer, and CNN-based learning (98% accuracy) enables real-time robotic hand control, with direct relevance to rehabilitation, prosthetics, and human–robot collaboration.