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用于智能生物识别系统的具有超弹性结构的纳米复合多模态传感器阵列

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

机构信息

Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.

Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA.

出版信息

Small. 2024 Nov;20(48):e2405224. doi: 10.1002/smll.202405224. Epub 2024 Sep 9.

Abstract

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

摘要

一种结合压力和接近感应的多模态传感器阵列,由于其在心肺健康和睡眠相关生物特征的普遍监测中的重要性,引起了相当大的关注。然而,常见传感器的灵敏度和动态范围通常不足以检测微妙的身体信号。本研究介绍了一种用于检测多种人体生物特征的新型电容纳米复合接近-压力传感器(NPPS)。NPPS 由碳纳米管纸复合(CPC)电极和包含在 MWCNT 涂层各向异性框架中的渗透多壁碳纳米管(MWCNT)泡沫组成。CPC 电极中的断裂纤维增强了电场,从而实现了对接近和压力的高灵敏度检测。当压力施加到传感器上时,MWCNT 泡沫和各向异性变形的协同作用会放大灵敏度。简单且可大规模生产的制造协议允许构建高度灵敏的传感器阵列,以监测人体存在、睡眠姿势和生命体征,包括心冲击图(BCG)。借助机器学习算法,传感器阵列无需干预即可准确检测血压(BP)。这一进展有望在睡眠或驾驶期间实现不受限制的生命体征监测。

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