Morphology-Programmable Orthogonally Aligned Silicon Nanowire Arrays for Integrated Strain-Temperature Bimodal...

Wearable multimodal sensors enable advanced health and motion monitoring but often face a trade-off between signal crosstalk and structural complexity. Herein, we report a strain-temperature bimodal sensor based on morphology-programmable, orthogonally aligned silicon nanowire (SiNW) arrays that intrinsically decouple the two modalities within a single-material platform. Unlike conventional designs relying on shared channels or heterogeneous stacks, our architecture employs an in-plane solid-liquid-solid mechanism to grow orthogonally arranged arrays of serpentine and straight SiNW channels, for strain and temperature sensing, respectively. The sensor achieves a high strain sensitivity (gauge factor up to 155) and a broad temperature detection range (20-97 °C) while maintaining stable operation over 40,000 stretching cycles, enabling simultaneous monitoring of skin temperature and joint motion. Furthermore, a convolutional neural network (CNN)-assisted classifier precisely identifies complex stimuli with 95% accuracy. This structurally integrated platform establishes a scalable pathway for multimodal sensing, advancing next-generation wearable electronics and human-machine interaction systems.