Self-Healing Pd NPs@MXene/PVA hydrogel strain sensor: High sensitivity and wide range for electronic skin

The Pd NPs@MXene/PVA hydrogel strain sensor achieves an ultra-wide strain range from 10% to 1680% with high gauge factor of 18.4. • The sensor demonstrates self-healing with 98.4% efficiency in 25 s, retains 75% of tensile strength and 89% strain capacity. • Hydrogen bonding and Pd NPs enhance mechanical performance, with tensile strength of 60.8 kPa and fracture strain of 1680%. • Sensor array integrated into smart glove enables object grasping and 0–9 gesture recognition, showing potential for human–machine interfaces. Hydrogel strain sensors are promising for electronic skin due to their flexibility and biocompatibility, yet balancing mechanical robustness, sensing performance, and self-healing remains challenging. Here, we develop an optimized Pd nanoparticles (Pd NPs)-decorated MXene/polyvinyl alcohol (PVA) hydrogel strain sensor (Pd NPs@MXene/PVA) with enhanced multifunctionality. Mechanically, the hydrogel achieves a tensile strength of 60.8 kPa and fracture strain of 1680%, outperforming pristine PVA hydrogel (17.3 kPa, 800%) through dynamic borate ester, hydrogen bonding and Pd NP reinforcement. For sensing, it provides repeatable responses over 100%–1500% strain, with cyclic stability validated at 200%–600%. Detectable responses from 10% to 1680% were observed under monotonic loading, and the maximum GF reached 18.4 in the 1200%–1680% regime. The device responds rapidly (600–700 ms) and was evaluated over 5000 stretching–releasing cycles, with the response becoming markedly more repeatable after an initial conditioning stage. Notably, it achieves 98.4% electrical self-healing efficiency within 25 s, retaining 75% tensile strength and 89% strain capability after healing, which is attributed to the dynamic borate ester bonds between PVA chains and tetraborate ions, together with reversible hydrogen-bonding interactions among PVA, MXene, and Pd NPs. Integrated into a smart glove, the sensor array distinguishes object grasps, generates distinguishable resistance patterns for digit gestures 0–9 and achieves high-accuracy MLP-based classification of digits 1–9, validating its practicality for human-machine interfaces. This work advances hydrogel sensor design by integrating Pd NPs for optimized multifunctionality, offering a robust platform for next-generation electronic skin. Download: Download high-res image (152KB) Download: Download full-size image