Multilayer Janus-based printed flexible electrodes with broad bioelectrical compatibility for multi-modal health monitoring

IF 12.5 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Wen Xu, Xiaolin Guo, Lingling Wu, Haonan Jia, Pei Pan, Jun Li, Jianhua Zhang
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引用次数: 0

Abstract

Flexible bioelectronic systems have shown great potential for continuous physiological monitoring, yet signal instability and insufficient multimodal coordination under dynamic conditions remain key challenges. Here, we report a Janus Flexible Electrode System (JFES) integrating printed Ag/PEDOT composite electrodes, electrospun thermoplastic polyurethane (TPU) membranes, and an elastomeric adhesive interlayer for simultaneous acquisition of electrophysiological and strain signals. Benefiting from the dual-surface functional partitioning and stress-buffered multilayer structure, the device exhibits excellent flexibility, strong skin adhesion, stable electrical performance, and a high EMG signal-to-noise ratio of approximately 36 dB. Through feature-level EMG–strain fusion assisted by a lightweight convolutional neural network (CNN), the system achieves a gesture-recognition accuracy of 94.34%, outperforming the EMG-only model. Furthermore, in vivo experiments on anesthetized mice demonstrate the system's capability for synchronous recording of tail-pinch-associated cortical potentials and strain responses. Quantitative ECoG analysis shows stimulation-induced redistribution of spectral power within 0.5–30 Hz, while the strain channel captures corresponding mechanical responses. This study provides a scalable and reliable Janus electrode strategy for multimodal bioelectronic interfaces, bridging electrophysiological and mechanical signal monitoring toward intelligent health diagnostics and neurophysiological applications.
多层janus印刷柔性电极具有广泛的生物电兼容性,用于多模态健康监测
柔性生物电子系统在连续生理监测方面显示出巨大的潜力,但在动态条件下信号不稳定和多模态协调不足仍然是主要挑战。在这里,我们报道了一种Janus柔性电极系统(JFES),该系统集成了印刷Ag/PEDOT复合电极,电纺热塑性聚氨酯(TPU)膜和弹性体胶粘剂中间层,用于同时采集电生理和应变信号。得益于双表面功能分区和应力缓冲多层结构,该器件具有优异的柔韧性、强的皮肤粘附性、稳定的电学性能和约36 dB的高肌电信号信噪比。通过轻量级卷积神经网络(CNN)辅助的特征级肌电信号应变融合,该系统实现了94.34%的手势识别准确率,优于仅肌电信号模型。此外,对麻醉小鼠的体内实验表明,该系统能够同步记录与尾巴夹紧相关的皮质电位和应变反应。定量ECoG分析显示,在0.5-30 Hz范围内,刺激引起的谱功率重分布,而应变通道捕获相应的力学响应。该研究为多模态生物电子接口提供了一种可扩展且可靠的Janus电极策略,将电生理和机械信号监测连接到智能健康诊断和神经生理学应用中。
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来源期刊
Chemical Engineering Journal
Chemical Engineering Journal 工程技术-工程:化工
CiteScore
21.70
自引率
9.30%
发文量
6781
审稿时长
2.4 months
期刊介绍: The Chemical Engineering Journal is an international research journal that invites contributions of original and novel fundamental research. It aims to provide an international platform for presenting original fundamental research, interpretative reviews, and discussions on new developments in chemical engineering. The journal welcomes papers that describe novel theory and its practical application, as well as those that demonstrate the transfer of techniques from other disciplines. It also welcomes reports on carefully conducted experimental work that is soundly interpreted. The main focus of the journal is on original and rigorous research results that have broad significance. The Catalysis section within the Chemical Engineering Journal focuses specifically on Experimental and Theoretical studies in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. These studies have industrial impact on various sectors such as chemicals, energy, materials, foods, healthcare, and environmental protection.
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