Demonstrates high conductivity and biocompatibility in bioelectronics, suggesting a new approach for wearable health monitoring.
Developing flexible bioelectronics that seamlessly integrate with human skin requires materials that are not only soft and conductive but also biocompatible and sustainable. Silk fibroin (SF), a natural protein with inherent biocompatibility and hierarchical structure, holds promise for such applications, yet its mechanical properties are mismatched with soft biological tissues, and it lacks intrinsic conductivity, limiting its standalone use. Inspired by nature’s use of Ca 2+ for both structural reinforcement and signal transduction, we report a green supramolecular hydrogel built upon SF as the core component, combined with poly(vinyl alcohol) (PVA) and Ca 2+ via a one-step freeze–thaw method. Density functional theory (DFT) calculations confirm that the three components engage in extensive noncovalent interactions, including hydrogen bonding, ionic coordination, and van der Waals forces, collectively constructing a stable yet dynamic cross-linked network. Within this network, Ca 2+ functions both as a dynamic cross-linker to enhance mechanical properties and as a charge carrier to enable ionic conduction. The resulting hydrogel exhibits an exceptional combination of skin-like mechanical properties, with a Young’s modulus of 0.114 MPa, a tensile strength of 0.408 MPa, and an elongation at break of 407%. It also features a high ionic conductivity of 2.330 S/m, rapid self-healing, broad adhesion to diverse substrates, and excellent biocompatibility, with cell viability exceeding 80% at concentrations of 0.78–25 mg/mL. When employed as a flexible electrode, the hydrogel enables high-fidelity, real-time monitoring of human electrocardiogram and electromyogram signals, with signal intensity closely correlated to physiological activity. This work presents a bioinspired strategy for developing high-performance and biocompatible sensing materials, advancing the frontier of wearable health monitoring and human–machine interfaces.
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Ye et al. (2026) studied this question.
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