Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

被引:45
作者
Huang, Manjuan [1 ]
Zhu, Minglu [1 ]
Feng, Xiaowei [1 ]
Zhang, Zixuan [2 ,3 ,4 ]
Tang, Tianyi [1 ]
Guo, Xinge [2 ,3 ,4 ]
Chen, Tao [1 ]
Liu, Huicong [1 ]
Sun, Lining [1 ]
Lee, Chengkuo [2 ,3 ,4 ,5 ,6 ]
机构
[1] Soochow Univ, Sch Mech & Elect Engn, Jiangsu Prov Key Lab Adv Robot, Suzhou 215123, Peoples R China
[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
[3] Natl Univ Singapore, Suzhou Res Inst NUSRI, Suzhou 215123, Peoples R China
[4] Natl Univ Singapore, Ctr Intelligent Sensors & MEMS CISM, Singapore 117608, Singapore
[5] Natl Univ Singapore, NUS Grad Sch, Singapore 119077, Singapore
[6] Natl Univ Singapore, Integrat Sci & Engn Program ISEP, Singapore 119077, Singapore
关键词
artificial intelligence of things (AIoT); self-powered sensor; piezoelectric generator; machine learning; status monitoring; FREQUENCY-UP-CONVERSION; TRIBOELECTRIC NANOGENERATOR; HYBRID; VIBRATIONS; SYSTEM;
D O I
10.1021/acsnano.2c11366
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via 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 via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-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(3) 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 degrees 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.
引用
收藏
页码:6435 / 6451
页数:17
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