Deep-Eutectic-Solvent-Enabled Polyampholyte/MXene Eutectogels for Environmentally Tolerant Wearable Respiratory Monitoring

Conductive hydrogels are promising for wearable electronics; however, freezing and dehydration of their water-rich networks often compromise mechanical compliance, electrical conductivity, and sensing reliability. Here, we report a deep-eutectic-solvent-enabled polyampholyte/MXene eutectogel constructed from a betaine/ethylene glycol deep eutectic solvent (DES), a dynamically crosslinked polyampholyte network formed from sodium p -styrenesulfonate (NaSS) and quaternized dimethylaminoethyl acrylate (DMAEA-Q), and MXene nanosheets. MXene establishes the principal conductive pathways, while DES-mediated molecular interactions alter the local electronic structure of representative polyampholyte-associated complexes. Density functional theory calculations show that replacing water with DES redistributes the frontier molecular orbitals and decreases the calculated HOMO–LUMO energy difference of the NaSS-DMAEA-Q complex from 4.60 to 4.34 eV, consistent with a more favorable local charge-transfer environment. Correspondingly, at the same MXene loading, the DES-based eutectogel reaches a conductivity of 1.51 S/m, more than twice that of the water-based hydrogel (0.71 S/m). The DES hydrogen-bonding network also suppresses crystallization and solvent evaporation, while reversible ionic-pair interactions provide high deformability and adhesion. This formulation exhibits a fracture strain of approximately 630%, an adhesion strength of 47.9 kPa, a crystallization peak at –74.3 ° C, and 94% mass retention after 30 days. The resulting sensor enables strain, pressure, temperature, and respiratory monitoring. Furthermore, integration with an STM32-based signal-acquisition system and a deep neural network enables four-class recognition of respiratory signals collected from the mouth, nose, abdomen, and chest, achieving an internal validation accuracy of 95.06%, while a threshold-based warning module provides real-time identification of apnea-like respiratory interruptions.