Ultrastretchable, fatigue-resistant eutectogel with hierarchical bonding for advanced wearable monitoring
Source abstract
Abstract
Wearable healthcare and IoT systems require conductors that are highly stretchable, skin-conformal, and capable of stable sensing under dynamic mechanical stress. However, conventional ionic conductors—such as hydrogels and eutectogels—often suffer from low ionic conductivity, poor fatigue resistance, and mechanical fragility due to inherent trade-offs between electrical and mechanical properties. Here, we present an ultrastretchable, fatigue-resistant organic mixed ionic–electronic conductor (OMIEC) eutectogel, engineered via a hierarchical bonding architecture. This design integrates dynamic hydrogen bonding within a polymerizable deep eutectic solvent (PDES) matrix and hydrophobic interactions from embedded PEDOT-based conductive domains. The synergistic interplay between these networks significantly enhances mechanical toughness, fracture resistance, electrical conductivity, and electromechanical sensitivity. The eutectogel demonstrates a 66-fold increase in conductivity, a 6.2-fold enhancement in fracture energy, and a 4.5-fold improvement in toughness compared to conventional ionic conductors, while maintaining ultralow electromechanical hysteresis (≤ 1%) under strains of up to 1,500%. Furthermore, the material exhibits autonomous self-healing and retains its functionality more than 100,000 stretch–release cycles. These multifunctional properties enable precise and robust monitoring of physiological motion, temperature variation, and complex human gestures under diverse mechanical stimuli and dynamic environmental conditions. The proposed OMIEC eutectogel thus represents a promising platform for next-generation wearable electronics in healthcare, soft robotics, and IoT applications.
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Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Textile materials and evaluations · Tactile and Sensory Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (78)
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