JOURNAL OF MATERIALS SCIENCE
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2025
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IF 3.9
Zhang Kai,An Xuyao,Wang Chunnan,Wang Yijie,Sun Zhiyuan,Ling Tianyi,Lu Shuli,Sun Shuqing
Cardiovascular disease, characterized by acute onset and high mortality rates, necessitates continuous daily monitoring of blood pressure (BP). Recently, advancements in flexible pressure sensing and intelligent wearable technology offer new solutions for non-invasive ambulatory BP monitoring. However, measuring BP with pressure sensors remain challenging due to the weak nature of skin pulse waves, making it difficult to obtain high-fidelity signals. This work introduced a piezoresistive sensor based on polydopamine and carbon nanotubes, which demonstrated high sensitivity (6.23 kPa −1 ), excellent linearity (R 2 = 0.996) across a broad pressure range (0–350 kPa), and maintained a stable electrical response after 10000 compression cycles. It featured a low detection limit (less than 100 Pa) and a rapid response time (100 ms), enabling the detection of pulse waves at multiple regions. Additionally, this study established a multi-parameter BP estimation model using the pulse wave velocity method, which significantly outperformed three simple models in terms of fitting accuracy. Concurrently, a dual-channel pulse wave measurement system was developed to estimate BP values, and its results were consistent with those from commercial sphygmomanometers. This work provided a simple, low-cost approach for the long-term monitoring of cardiovascular diseases, presenting a competitive alternative to current complex systems. Graphical abstract
研究领域:
生物医学工程
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材料科学
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心血管健康
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产品关联
DOI
高影响因子
Nano-Micro Letters
·
2024
·Vol. 17
·期 1
·1-15
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IF 31.6
Yin Hao,Li Yanting,Tian Zhiying,Li Qichao,Jiang Chenhui,Liang Enfu,Guo Yiping
Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.
研究领域:
生物医学工程
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传感器技术
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智能材料
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结构健康监测
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机器人学
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可穿戴设备
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力学感知
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转化医学
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DOI
高影响因子
Microsystems & Nanoengineering
·
2025
·Vol. 11
·期 1
·1-13
·
IF 9.9
Zhang Xikuan,Chai Jin,Xu Lingxiao,Mei Shixuan,Wang Xin,Zhao Yunlong,Xue Chenyang,Wang Yongjun,Cui Danfeng,Zhang Zengxing,Zhang Haiyan,Gao Libo
The long-term monitoring of respiratory status is crucial for the prevention and diagnosis of respiratory diseases. However, existing continuous respiratory monitoring devices are typically bulky and require either chest strapping or proximity to the nasal area, which compromises user comfort and may disrupt the monitoring process. To overcome these challenges, we have developed a flexible, attachable, lightweight, and miniaturized system designed for extended wear on the wrist. This system incorporates signal acquisition circuitry, a mobile client, and a deep neural network, facilitating long-term respiratory monitoring. Specifically, we fabricated a highly sensitive (11,847.24 kPa −1 ) flexible pressure sensor using a screen printing process, which is capable of functioning beyond 70,000 cycles. Additionally, we engineered a bidirectional long short-term memory (BiLSTM) neural network, enhanced with a residual module, to classify various respiratory states including slow, normal, fast, and simulated breathing. The system achieved a dataset classification accuracy exceeding 99.5%. We have successfully demonstrated a stable, cost-effective, and durable respiratory sensor system that can quantitatively collect and store respiratory data for individuals and groups. This system holds potential for everyday monitoring of physiological signals and healthcare applications.
研究领域:
生物医学工程
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呼吸生理学
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可穿戴传感器
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转化医学
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健康监测
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产品关联
DOI
PubMed
高影响因子
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