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基于 P(VDF-TrFE)/KNN/GR 复合压电薄膜的柔性纳米心音传感器的设计与应用,用于心脏病诊断。

Design and application of a flexible nano cardiac sound sensor based on P(VDF-TrFE)/KNN/GR composite piezoelectric film for heart disease diagnosis.

机构信息

School of Electronics and Information Engineering, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China.

School of Communication Engineering, Hangzhou DIANZI University, Hangzhou 310018, People's Republic of China.

出版信息

Nanotechnology. 2023 Nov 29;35(7). doi: 10.1088/1361-6528/ad0502.

Abstract

The paper proposes a flexible micro-nano composite piezoelectric thin film. This flexible piezoelectric film is fabricated through electrospinning process, utilizing a combination of 12 wt% poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)), 8 wt% potassium sodium niobate (KNN) nanoparticles, and 0.5 wt% graphene (GR). Under cyclic loading, the composite film demonstrates a remarkable increase in open-circuit voltage and short-circuit current, achieving values of 36.1 V and 163.7 uA, respectively. These values are 5.8 times and 3.6 times higher than those observed in the pure P(VDF-TrFE) film. The integration of this piezoelectric film into a wearable flexible heartbeat sensor, coupled with the RepMLP classification model, facilitates heartbeat acquisition and real-time automated diagnosis. After training and validation on a dataset containing 2000 heartbeat samples, the system achieved an accuracy of approximately 99% in two classification of heart sound signals (normal and abnormal). This research substantially enhances the output performance of the piezoelectric film, offering a novel and valuable solution for the application of flexible piezoelectric films in physiological signal detection.

摘要

本文提出了一种柔性微纳复合压电薄膜。该柔性压电薄膜是通过静电纺丝工艺制备的,采用了 12wt%聚偏氟乙烯-三氟乙烯共聚物(P(VDF-TrFE))、8wt%铌酸钾钠(KNN)纳米粒子和 0.5wt%石墨烯(GR)的组合。在循环加载下,复合薄膜的开路电压和短路电流显著增加,分别达到 36.1V 和 163.7uA。这些值分别是纯 P(VDF-TrFE)薄膜的 5.8 倍和 3.6 倍。将这种压电薄膜集成到可穿戴的柔性心跳传感器中,并结合 RepMLP 分类模型,实现了心跳的采集和实时自动化诊断。在包含 2000 个心跳样本的数据集上进行训练和验证后,该系统在两种心音信号(正常和异常)的分类中达到了约 99%的准确率。本研究显著提高了压电薄膜的输出性能,为柔性压电薄膜在生理信号检测中的应用提供了一种新颖而有价值的解决方案。

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