ABSTRACT Achieving non‐invasive and high‐fidelity electrophysiological recording, particularly electroencephalography (EEG), on dynamic and irregular human skin remains a central challenge in soft bioelectronics, as materials rarely reconcile liquid‐like adaptability with solid‐like stability. Here, we overcome this limitation by designing a viscoelastic ionogel governed by a dynamic enthalpy‐entropy balance. Salt‐bridge hydrogen bonds form a low‐entropy and high‐interaction network, intrinsically limiting the capacity for entropic energy storage. This network then self‐organizes with a soft phase into a bicontinuous nanostructure. Acting as a mechanical parallel circuit, this architecture introduces a broad molecular relaxation spectrum, providing broadband enthalpic dissipation and realizing broadband enthalpy‐entropy compensation. Consequently, the ionogel exhibits a frequency‐independent viscoelastic plateau (G′≈G′′) spanning over nine orders of magnitude in frequency (10 −4 to 10 5 Hz) and a wide temperature range (−30°C to 40°C). The ionogel reduces skin‐electrode impedance by more than an order of magnitude compared to commercial electrodes and maintains high‐fidelity electrophysiological recordings during 72‐h continuous wear. Integrated with a deep learning framework, it enables high‐precision decoding of EEG signals, achieving 95% accuracy in classifying eight distinct emotional states. This work establishes a generalizable thermodynamic design principle for soft bioelectronic interfaces, offering broad potential for neural diagnostics, emotional monitoring, and wearable neuroprosthetics.
Zhang et al. (Thu,) studied this question.