School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China.
State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing100190, China.
ACS Nano. 2023 Mar 28;17(6):5673-5685. doi: 10.1021/acsnano.2c11897. Epub 2023 Jan 30.
Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.
具有高灵敏度、宽线性范围和快速响应时间的压力传感器对于构建直接检测和识别医疗和健康应用中脉搏信号的智能疾病诊断系统至关重要。然而,传统的压力传感器灵敏度有限,响应范围不理想。我们提出了一种基于聚酰亚胺/多壁碳纳米管-聚二甲基硅氧烷纳米复合材料/聚酰亚胺(PI/MPN/PI)三明治结构的多通道柔性脉冲感知阵列压力传感器,可用于远程疾病诊断。此外,我们建立了分子水平的机械模型,并指导了 MPN 的制备。在结构水平上,我们通过具有不同上下表面的金字塔状双层微结构实现了高灵敏度(35.02 kPa)和宽响应范围(0-18 kPa)。在器件水平上集成了一个 27 通道(3×9)高密度传感器阵列,可提取脉搏的时空分布信息。此外,还开发了两种智能算法,用于提取六维脉搏信息和自动脉搏识别(识别率达到 97.8%)。结果表明,智能疾病诊断系统在可穿戴医疗保健设备中有很大的应用潜力。