Self-Powered Wireless Piezoelectric Sensor Based on Polyamide Elastomer/BaTiO3 for Machine Learning-Assisted Human Motion Monitoring

材料科学 纳米复合材料 压电 弹性体 灵活性(工程) 色散(光学) 聚酰胺 热塑性聚氨酯 压电传感器 聚合物 陶瓷 复合材料 纳米发生器 可穿戴计算机 人体运动 纳米技术 执行机构 工作(物理) 无线 热塑性弹性体 聚合物纳米复合材料 智能材料 纳米颗粒 超声波传感器 声学 基质(化学分析) 领域(数学) 人工肌肉 热塑性塑料
作者
Weifang Zhou,Zhihao Chen,Xin Yuan,Hui Yu,Yuancheng Zhang,Wei Zhao,Xin Li,Xiaomeng Zhang,Zhe Cui,Peng Fu,Xinchang Pang,Minying Liu
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:17 (41): 57500-57515 被引量:3
标识
DOI:10.1021/acsami.5c12234
摘要

In the field of self-powered sensing, the need for wearing comfort and flexibility of the material is increasing. Although piezoelectric ceramics have excellent piezoelectric properties, their poor flexibility limits their application range. In the quest for better wearing comfort, nanocomposite polymers using elastomers as polymer matrices are rapidly emerging. However, there still exists a challenge to achieve uniform dispersion of nanofillers in the polymer matrix due to their significant difference in surface energy. In this work, a novel thermoplastic polyamide elastomer (TPAE) based nanocomposite with strong interfacial strength and uniform dispersion was proposed, which was achieved through incorporating γ-aminopropyltriethoxysilane (KH550)-functionalized BaTiO3 nanoparticles (KH550@BTO) into in situ polycondensation of TPAE for flexible piezoelectric nanogenerators (PENGs) with sensing capability. The PENGs not only possessed excellent mechanical properties (elongation at break ∼ 400%) but also exhibited good piezoelectric behavior (VOC ∼ 19 V, ISC ∼ 121 nA). Moreover, the PENGs exhibited excellent durability, stability (6000 cycles without degradation), and rapid response time (∼21 ms), which was superior to those of most reported BTO-based nanocomposites. Based on the performance, the PENG could be used to monitor human motions after the assembly of wireless wearable devices, and different badminton serving postures were further recognized by the assistance of machine learning. This work provides an innovative approach for the preparation of soft piezoelectric nanocomposites, which is helpful for self-powered and wireless flexible sensors with better performance and more comfortable use in human motion monitoring and recognition.
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