用于自供电物体识别的混合纳米发电机

IF 6.7 3区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Journal of Science: Advanced Materials and Devices Pub Date : 2024-02-18 DOI:10.1016/j.jsamd.2024.100693
Junghun Jo , Swati Panda , Nayoon Kim , Sugato Hajra , Subhin Hwang , Heewon Song , Jyoti Shukla , Basanta K. Panigrahi , Venkateswaran Vivekananthan , Jiho Kim , P. Ganga Raju Achary , Hohyum Keum , Hoe Joon Kim
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引用次数: 0

摘要

能量收集系统,包括压电(PENG)、三电(TENG)和热电(PYNG)纳米发电机技术,已成为未来主要的能源解决方案之一。能量收集消除了对传统电池的需求,并鼓励采用生态友好型替代品。本研究报告了用于混合能量收集的水热合成四方对称性氧化钡(BTO)颗粒。BTO 颗粒与不同重量百分比的 PDMS 结合形成柔性复合薄膜。15wt% BTO-PDMS 复合材料/铝混合器件(PENG-TENG)可产生 100 V 的峰值电压、980 nA 的电流和 17 nC 的电荷,在 100 MΩ 时可产生 33.64 μW 的峰值功率输出。此外,整合这种 HNG(外部杂化)可产生 101 V 和 980 nA 的输出,证明了其实用性。HNG 还可用于在不同温度下通过接触各种物体进行互动。BTO 的热释电行为允许直接对物体进行热感应。产生的信号通过基于卷积神经网络(CNN)的物体识别系统进行处理,该系统对各种物体的分类准确率高达 99.27%。外部混合提高了能源效率,在可持续技术应用方面迈出了一大步。这项研究为开发混合能量收集器铺平了道路,并可进一步用于极其精确的无电池物体识别系统。这种独特的混合纳米发电机结合了热释电、压电和三电元件,是一种自供电物体检测的新方法。外部混合提高了能源效率,在可持续技术应用方面迈出了一大步。
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Hybrid nanogenerator for self-powered object recognition

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 μW at 100 MΩ. 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.

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来源期刊
Journal of Science: Advanced Materials and Devices
Journal of Science: Advanced Materials and Devices Materials Science-Electronic, Optical and Magnetic Materials
CiteScore
11.90
自引率
2.50%
发文量
88
审稿时长
47 days
期刊介绍: In 1985, the Journal of Science was founded as a platform for publishing national and international research papers across various disciplines, including natural sciences, technology, social sciences, and humanities. Over the years, the journal has experienced remarkable growth in terms of quality, size, and scope. Today, it encompasses a diverse range of publications dedicated to academic research. Considering the rapid expansion of materials science, we are pleased to introduce the Journal of Science: Advanced Materials and Devices. This new addition to our journal series offers researchers an exciting opportunity to publish their work on all aspects of materials science and technology within the esteemed Journal of Science. With this development, we aim to revolutionize the way research in materials science is expressed and organized, further strengthening our commitment to promoting outstanding research across various scientific and technological fields.
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