Junjie Wang, Binbin Zhai, Jing Zhang, Hanyang Ning, Qi He, Tinghao Wu, Kang Li, Chi Zhang, Yanyan Luo, Aiping Chi, Wei Ren, Zhongshan Liu*, Yu Fang*. Advanced Functional Materials 2026, e24980, DOI: 10.1002/adfm.202524980

Epidermal electrophysiological signals—such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG)—act as the body’s “telegraph”, serving as vital tools not only for medical diagnosis but also for health monitoring in brain and exercise research. Recording these signals requires epidermal electrodes adhered to the skin, which are classified as either wet or dry. While gel-based wet electrodes are self-adhesive, they suffer from poor gas permeability and dehydration, limiting their use in prolonged monitoring. To overcome these limitations, dry electrodes with inherent stretchability, breathability, and skin-conformality have attracted significant interest.

Figure 1. Characterization of the PEG-CTH nanofilm

In this work, we present an ultrathin dry epidermal electrode fabricated from a crosslinked nanofilm coated with silver nanowires. This nanofilm, denoted as PEG-CTH, is synthesized via air-liquid interfacial polymerization between aldehyde-terminated polyethylene glycol (PEG-CHOs) and a calix[4]arene derivative. The acyl-hydrazone and polyethylene glycol moieties within the nanofilm promote strong skin adhesion.

Figure 2. PEG-CTH nanofilm-based epidermal electrodes for bio-signals monitoring

After being spray-coated with silver nanowires, the nanofilm electrode exhibits stable conductivity even under 100% strain. Furthermore, the adhesion force of the nanofilm electrode is maintained before and after sweating. We assess its mechanical robustness through multimodal sensing of human body motions, including strain and shear force. Finally, we demonstrate the practical utility of these nanofilm epidermal electrodes by acquiring high-quality electrocardiogram and electromyogram signals, as well as by monitoring motions of both large muscle and fine muscle, where the latter is one of the most demanding tests for flexible electrodes. By combining them with a deep learning algorithm, high recognition accuracy (100%) is achieved for five different finger gestures.

First Author: Wang Junjie, master’s student, Shaanxi Normal University

Correspondence Authors: Prof. Fang Yu, Assoc. Prof. Liu Zhongshan, Shaanxi Normal University

Full Text Link: https://doi.org/10.1002/adfm.202524980