Hybrid nanogenerator for self-powered object recognition

被引:2
|
作者
Jo, Junghun [1 ]
Panda, Swati [1 ]
Kim, Nayoon [1 ]
Hajra, Sugato [1 ]
Hwang, Subhin [1 ]
Song, Heewon [1 ]
Shukla, Jyoti [2 ]
Panigrahi, Basanta K. [3 ]
Vivekananthan, Venkateswaran [4 ]
Kim, Jiho [5 ]
Achary, P. Ganga Raju [6 ]
Keum, Hohyum [7 ]
Kim, Hoe Joon [1 ]
机构
[1] Daegu Gyeongbuk Inst Sci & Technol, Dept Robot & Mechatron Engn, Daegu 42988, South Korea
[2] SKIT M&G, Dept Elect Engn, Jaipur 302017, India
[3] Siksha O Anusandhan Univ, Dept Elect Engn, Bhubaneswar 751030, India
[4] Koneru Lakshmaiah Educ Fdn, Ctr Flexible Elect, Dept Elect & Commun Engn, Vaddeswaram 522302, Andhra Pradesh, India
[5] Hongik Univ, Dept Mech & Syst Design Engn, Seoul 04066, South Korea
[6] Siksha O Anusandhan Univ, Dept Chem, Bhubaneswar 751030, India
[7] Korea Inst Ind Technol KITECH, Digital Hlth Care R&D Dept, Cheonan 31056, South Korea
来源
基金
新加坡国家研究基金会;
关键词
Energy; Triboelectric; Pyroelectric; Hybrid system; Object recognition; ENERGY; BATIO3;
D O I
10.1016/j.jsamd.2024.100693
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Energy harvesting systems, including piezoelectric (PENG), triboelectric (TENG), and pyroelectric (PYNG) nanogenerator technologies, have emerged as one of the major future energy solutions. Energy harvesting eliminates the need for conventional batteries and encourages eco-friendly alternatives. This study reports hydrothermally synthesized BaTiO3 (BTO) particles with a tetragonal symmetry for hybrid energy harvesting. BTO particles are incorporated with PDMS at various wt% to form a flexible composite film. The 15 wt% BTO-PDMS composite/Al hybrid device (PENG-TENG) produces a peak voltage of 100 V, a current of 980 nA, and a charge of 17 nC, generating a peak power output of 33.64 mu W at 100 M omega. Furthermore, integrating this HNG (external hybridization) yielded an output of 101 V and 980 nA, demonstrating practical applicability. HNG is also employed to interact by touching various objects at different temperatures. The pyroelectric behavior of BTO allows direct thermal sensing of the object. The signals produced are processed using a convolutional neural network (CNN)-based object recognition system, which achieved a remarkable classification accuracy of 99.27% for various objects. External hybridization improves energy efficiency, representing a huge step forward in sustainable technology applications. This research paves the way for developing hybrid energy harvesters and can be employed further for extremely precise battery-free object recognition systems. This unique hybrid nanogenerator, which combines pyroelectric, piezoelectric, and triboelectric components, represents a new method of self-powered object detection. External hybridization improves energy efficiency, representing a huge step forward in sustainable technology applications.
引用
收藏
页数:8
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