材料科学
信号(编程语言)
联锁
手指敲击
纳米技术
可穿戴计算机
压力传感器
石墨烯
可穿戴技术
干扰(通信)
生物医学工程
仿生学
电容感应
拉伤
计算机科学
多孔性
工件(错误)
电极
声学
纳米复合材料
触觉传感器
异质结
软机器人
作者
Dijie Yao,Qiuhua Yu,Yifan Zheng,Huizhi Chen,Cheng Shen,Jiajia Yang,Zhenwen Liang,Kai Tao,Gang Chen,Liu Fei,Fengwei Huo,Junyou Yang,Jin Wu
摘要
Pressure sensors, with their immense potential for detecting fine physiological activities and monitoring neurological disease-related signals, face significant challenges, including the occurrence of signal artifacts during stretching and low comfort due to poor breathability. Here, a bioinspired interlocking hierarchical heterostructure (BIHH) is synergistically designed by anchoring a porous textile-microstructured ionogel to stretchable laser-induced graphene electrodes to simultaneously enhance strain insensitivity, pressure sensing performance, and wearable comfort. This modulus-gradient heterogeneous mechanical network induces interfacial strain partitioning: tensile strain dissipates along porous Ecoflex frameworks, while the pressure-sensitive domains are mechanically locked against deformation. Consequently, the sensor exhibits a mere 2% signal variation under 100% strain, maintaining consistent sensing capability even under severe stretching. Meanwhile, the hierarchical porous microstructure reconciles high-performance iontronic pressure sensing with skin-conformable breathability for array-based touch and human physiological signal perception. Furthermore, by integrating artificial intelligence recognition algorithms, a five-channel finger tapping assessment system achieves an accuracy of 97% in distinguishing different tapping patterns for the early auxiliary screening and rehabilitation of neurodegenerative diseases. This effective strategy opens a promising path for designing devices that reduce motion artifact interference while enhancing pressure sensing performance and comfort for on-skin physiological activity monitoring.
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