Smart epidermal electrophysiological electrodes:Materials,structures,and algorithms

Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare,particularly in continuous signal recording.However,simultaneously satisfying skin compliance,mechanical properties,environmental adaptation,and biocompatibility to avoid signal attenuation and motion artifacts is challenging,and accurate physiological feature extraction necessitates effective signal-processing algorithms.This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring,focusing on materials,structures,and algorithms.First,smart materials incorporating self-adhesion,self-healing,and self-sensing functions offer promising solutions for long-term monitoring.Second,smart meso-structures,together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality.Third,intelligent algorithms give smart electrodes a“soul,”facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals.Finally,the existing challenges and future opportunities for developing smart electrodes are discussed.Recognized as a crucial direction for next-generation epidermal electrodes,intelligence holds the potential for extensive,effective,and transformative applications in the future.

Highly Efficient Back‑End‑of‑Line Compatible Flexible Si‑Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced robotics.Leveraging 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption.Here,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia detection.With experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,respectively.The optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios.Moreover,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision application.To display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory functions.The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

Electronic tattoos based on large-area Mo2C grown by chemical vapor deposition for electrophysiology

Tattoo electronics has attracted intensive interest in recent years due to its comfortable wearing and imperceivable sensing,and has been broadly applied in wearable healthcare and human-machine interface.However,the tattoo electrodes are mostly composed of metal films and conductive polymers.Two-dimensional(2D)materials,which are superior in conductivity and stability,are barely studied for electronic tattoos.Herein,we reported a novel electronic tattoo based on large-area Mo_(2)C film grown by chemical vapor deposition(CVD),and applied it to accurately and imperceivably acquire on-body electrophysiological signals and interface with robotics.High-quality Mo_(2)C film was obtained via optimizing the distribution of gas flow during CVD growth.According to the finite element simulation(FES),bottom surface of Cu foil covers more stable gas flow than the top surface,thus leading to more uniform Mo_(2)C film.The resulting Mo_(2)C film was transferred onto tattoo paper,showing a total thickness of~3μm,sheet resistance of 60-150Ω/sq,and skin-electrode impedance of~5×10^(5)Ω.Such thin Mo_(2)C electronic tattoo(MCET in short)can form conformal contact with skin and accurately record electrophysiological signals,including electromyography(EMG),electrocardiogram(ECG),and electrooculogram(EOG).These body signals collected by MCET can not only reflect the health status but also be transformed to control the robotics for human-machine interface.

用于多模式人体体征信号采集人机界面的可回收水凝胶

离子导电水凝胶由于其在生物相容性、导电性和灵活性方面的独特优势,已成为可穿戴式人体运动检测传感器的最有希望的替代品之一.然而,目前的水凝胶电子器件仍然面临着不可重复使用性和单一信号检测功能的限制.在此,我们通过模拟生物体的网络结构和离子传导机制,制备了基于聚乙烯醇、海藻酸纤维和胶原蛋白的离子导电水凝胶,用于人体运动和电生理信号的双模式信号采集.通过海藻酸纤维和胶原蛋白的协同调节,水凝胶表现出类似皮肤的机械性能,具有低模量、高韧性和抗疲劳性,从而可以最大限度地提高水凝胶可穿戴传感器的穿着舒适度和运动检测能力.此外,具有与生物体相似离子传输行为的水凝胶导体可以用作表皮电极,收集包含重要人体生理信息的表皮电位.通过对可穿戴设备的合理设计和组装,水凝胶不仅可以连续、准确地识别全身运动信号,还可以采集肌电图、心电图等不同电生理信号.值得注意的是,这种基于非共价交联结构的水凝胶是完全可回收的,可以任意重组为新的电子器件,同时保持原有功能,提高水凝胶的可重复使用性,减少电子浪费.