Highly sensitive multimodal sensors based on ZnBr2-enhanced poly(ionic liquid) conductive elastomers

ZnBr 2 -regulated PIL/FPIL platform enables multimodal wearable sensing. • PIL-16.5 senses 0–800% strain with GF up to 5.54 and 1000-cycle stability. • Thin FPIL detects humidity, pulse, and respiration signals for wearable use. • K-means-assisted Random Forest identifies four breathing states with 95% accuracy. Soft ionic conductors are promising materials for wearable electronics, yet their practical application remains limited by the difficulty of balancing mechanical robustness, ionic transport, and multimodal sensing within a single material system. Herein, we report a ZnBr 2 -enhanced poly(ionic liquid) elastomer (PIL) prepared by UV polymerization of a [VEIM]Br-derived ionic precursor. Spectroscopic analyses, XPS, and DFT calculations indicate that ZnBr 2 reorganizes bromide-rich ionic environments within the PIL network, providing a structural basis for balancing network reinforcement and ion migration. The optimized PIL-16.5 exhibits a strain-sensing range of 0–800%, segmented gauge factors of up to 5.54, and stable electrical output over 1000 cycles at 100% strain. It also exhibits water-assisted self-healing at room temperature, recovering 89.5 ± 3.5% of its fracture strain and 87.4 ± 6.8% of its toughness, together with solution-assisted recyclability. Beyond mechanical sensing, the elastomer shows clear electrical responses to temperature and humidity, supporting multimodal sensing. This material supports wearable monitoring of joint movement, arterial pulse, and respiration. Furthermore, a machine-learning workflow was used to analyze respiratory signals, enabling differentiation among four respiratory states with an average classification accuracy of approximately 95% and demonstrating the feasibility of lightweight, data-driven physiological-state recognition. This work establishes a PIL-based sensing platform that combines large deformability, recoverability, multimodal responsiveness, and compatibility with data-driven analysis, showing potential for respiratory monitoring and wearable health applications. Download: Download high-res image (257KB) Download: Download full-size image