Recently, Associate Professor Yi Chen from the Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, in collaboration with Professor Wenlong Cheng from the University of Sydney and the team of Academician Ning Gu, published a research paper in the top-tier comprehensive international academic journal Science Advances titled “Morphology‑adaptive Au‒Ag Nanowire Elastronics for Integrated FlexoSERS and Bioelectrical Sensing.” The study proposes a morphology‑adaptive clustered nanowire hierarchical material, enabling a scalable integrated sensing platform across multi‑dimensional structures that simultaneously achieves enhanced optical detection (SERS) and bioelectrical signal acquisition. This work provides a core sensing material foundation for programmable design for next‑generation multimodal wearable health monitoring devices and human–machine interaction technologies.
Flexible biosensing technologies for health monitoring and disease diagnosis are providing critical support for precision health management, personalized intervention, and intelligent human‑machine interaction. In recent years, high‑performance sensing interfaces based on low‑dimensional nanomaterials have enabled highly sensitive and selective analysis of physiological and biochemical information within complex living systems, accelerating the translation of wearable and implantable devices toward in situ, real‑time monitoring. Among various physiological signals, electrophysiological signals such as electrocardiogram (ECG) and electromyogram (EMG) can reflect tissue excitability and rhythmic status in real time, serving as essential information sources for intelligent medical devices. However, electrical signals alone are insufficient to comprehensively reflect complex physiological and pathological processes. Particularly in dynamic health assessment scenarios, highly sensitive detection of trace biomarkers in biofluids such as sweat and saliva is equally indispensable. Thus, integrating electrophysiological and molecular information acquisition on the same flexible platform remains a key challenge in the development of multimodal bioelectronic technologies.
In recent years, surface‑enhanced Raman scattering (SERS) technology has demonstrated unique advantages in trace biofluid analysis due to its label‑free nature, high molecular specificity, and ultra‑high sensitivity. Theoretically, integrating SERS with electrophysiological detection into a single flexible device would enable multi‑scale, multi‑dimensional comprehensive analysis of physiological states. However, traditional SERS substrates are mostly rigid inorganic materials highly sensitive to deformation, while conventional electrophysiological electrodes rely on conductive gels, making long‑term stable operation difficult. The two functionalities have long been incompatible in terms of material systems and structural designs.
Addressing this critical challenge, this work presents a morphology‑adaptive flexible sensing platform based on clustered Au‒Ag nanowire arrays, achieving unified integration of SERS optical detection and stable electrophysiological signal acquisition across multi‑dimensional structures. Using a template‑guided self‑assembly strategy that requires no high‑temperature processing or complex transfer steps, highly ordered Au‒Ag nanowire arrays were directly constructed on substrates of different morphologies, including 1D needle‑like structures, 2D elastic films, and 3D porous sponges, enabling the same material system to adapt to various application scenarios. On a 2D elastic substrate, the nanowire array forms a flexible SERS interface with both high sensitivity and high uniformity; even under 100% tensile strain or after 2,500 repeated stretching cycles, the Raman signals remain stable, demonstrating excellent mechanical robustness and deformation insensitivity. In the 3D porous structure, the array further serves as a gel‑free dry bioelectrode, achieving long‑term stable, high‑signal‑to‑noise ratio ECG and EMG recordings. Using continuous ECG signals, the research team combined deep learning models to accurately identify sleep and wakefulness states, demonstrating the platform’s potential for intelligent sleep assessment. Meanwhile, the highly sensitive EMG signals can precisely capture subtle movements such as finger bending, keyboard typing, and mouse clicking, offering a new signal channel for non‑invasive human‑machine interaction. Through a unified nanostructure design, this study establishes an integration pathway for flexible SERS optical sensing and electrophysiological monitoring on 1D–3D morphologically diverse substrates.
Doctoral student Heng Zhang and Associate Professor Yi Chen from the School of Biological Science and Medical Engineering, Southeast University, are co‑first authors of the paper. Associate Professor Yi Chen, Professor Wenlong Cheng from the University of Sydney, and Academician Ning Gu are co‑corresponding authors. Southeast University is the first affiliation of this paper. This research was supported by the National Natural Science Foundation of China, the National Key Research and Development Program of China, the Natural Science Foundation of Jiangsu Province, and the Basic Research Program of Suzhou (Frontier Technology Research Program).
Link to the paper: https://www.science.org/doi/10.1126/sciadv.aec2162
