Huang Manjuan, Zhu Minglu, Feng Xiaowei, Zhang Zixuan, Tang Tianyi, Guo Xinge, Chen Tao, Liu Huicong, Sun Lining, Lee Chengkuo
School of Mechanical and Electrical Engineering, Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215123, China.
School of Future Science and Engineering, Soochow University, Suzhou 215123, China.
ACS Nano. 2023 Apr 11;17(7):6435-6451. doi: 10.1021/acsnano.2c11366. Epub 2023 Mar 20.
The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride--trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.
物联网人工智能(AIoT)的发展极大地推动了智慧城市全面感知和无缝通信的发展。作为基础,各种AIoT节点正面临着集成度低和可持续性差的问题。在此,提出了一种立方体设计的智能压电AIoT节点iCUPE,它集成了一个高性能能量收集和自供电传感模块,分别是基于微机电锆钛酸铅(PZT)厚膜的高频(HF)压电发电机(PEG)和基于聚偏二氟乙烯-三氟乙烯(P(VDF-TrFE))纳米纤维薄膜的低频(LF)-PEG。具有特定频率上转换(FUC)机制的LF-PEG和HF-PEG可确保在10 - 46 Hz的宽范围内持续供电,在1 g加速度下功率密度达到创纪录的17 mW/cm²。立方体设计允许三个FUC-PEG正交放置,以确保对来自不同方向的振动能源有广泛的响应。自供电三轴压电传感器(TPS)结合机器学习(ML),通过使用三个LF-PEG辅助三个正交压电传感单元,分别实现了加速度、频率和倾斜角度分辨率为0.01 g、0.01 Hz和2°的高精度多功能振动识别,提供了98% - 100%的高识别准确率。这项工作证明了开发一种基于ML的智能传感器用于谐振频率下加速度计和陀螺仪功能的可行性。所提出的可持续iCUPE具有高度可扩展性,可在不同环境下探索多功能传感和能量收集能力,这对于AIoT的实现至关重要。