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Ultra-stretchable, tough, and rapid self-healing MXene-composited hydrogels via dynamic interface-network coupling strategy for reliable wearable sensors and machine learning-assisted gesture recognition
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DOI：10.1016/j.compositesb.2026.114061.png)
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Abstract
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Conductive hydrogels with high stretchability, toughness, and rapid self-healing ability have emerged as promising materials for flexible wearable electronics, but face a trade-off between toughness and self-healing, as toughening strategies often suppress the network dynamics required for healing. Here, we report a dynamic interface-network coupling strategy to realize an ultra-stretchable, highly tough, and rapidly self-healing MXene-based conductive hydrogel by incorporating polydopamine-coated MXene nanosheets (PDA@MXene) into a dynamic poly(acrylamide-co-3-acrylamidophenylboronic acid) (P(AM-co-AAPBA)) network. The synergistic coupling between the dynamic interface of PDA@MXene with the polymer matrix and the tough reversible polymer network enables efficient energy dissipation while preserving rapid network reconstruction. The resulting hydrogel exhibits high stretchability (2396 ± 68%), high toughness (3.26 ± 0.37 MJ m−3), and rapid and efficient self-healing (≈83% within 7 min at room temperature). This hydrogel demonstrates good strain sensitivity over a broad range, excellent stability, and rapid recovery of sensing performance after damage, enabling reliable monitoring of diverse human activities. In addition, it can also serve as a skin-conformal bioelectrode for high-quality electrophysiological signal acquisition. Integration with machine learning further enables multiclass gesture recognition with an average accuracy of 96.37%. This work provides a general strategy for constructing highly tough and rapidly self-healing hydrogels for demanding applications.
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