可穿戴计算机
材料科学
气流
热电效应
稳健性(进化)
肺病
气道
慢性阻塞性肺病
机械通风
远程病人监护
计算机科学
医学
恶化
生物医学工程
无线传感器网络
声学
呼吸监测
气道阻塞
持续监测
电子工程
可穿戴技术
肺功能测试
预警系统
汽车工程
信号(编程语言)
作者
H. Z. Shi,Huixu Li,Hanzhi Fu,Chan Huang,Xiaohong Cao,Yu Xia,Shaokai Ji,Xinyang He,D X Zhang,Chao Wang,Xianhui Zhou,Z M Li,Heting Wu
出处
期刊:ACS Nano
[American Chemical Society]
日期:2026-06-05
卷期号:20 (24): 17463-17473
标识
DOI:10.1021/acsnano.6c03384
摘要
The dynamic management of chronic obstructive pulmonary disease (COPD) and related obstructive airway diseases, such as asthma, demands respiratory monitoring technologies that combine high spatiotemporal resolution, long-term stability, and broad applicability. Yet current approaches, including bulky clinical systems, thoracoabdominal strain sensors prone to motion artifacts, and hydrogel-based airflow sensors susceptible to drift from solvent evaporation, fall short of these requirements in terms of portability, robustness against interference, and durability. Here, we report a porous graphene-based foam sensor that decouples strain-temperature to achieve simultaneous, crosstalk-free detection of deformation and airflow temperature without complex signal processing. The foam sensor maintains high mechanical and thermoelectric stability after 15,000 cyclic compression at 80% strain. Using the produced thermoelectric signal, we define a novel small airway obstruction index (SAOI) that provides individualized early warning of COPD acute exacerbation risk, enabling timely, real-time adjustment of treatment regimens.
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