Green energy from working surfaces: a contact electrification–enabled data theft protection and monitoring smart table

Chandrasekhar, Arunkumar; Vivekananthan, Venkateswaran; Khandelwal, Gaurav; Kim, Woo Joong; Kim, Sang‐Jae

doi:10.1016/j.mtener.2020.100544

Cited by 24 publications

(14 citation statements)

References 38 publications

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“…Conventional approaches heavily rely on power supplies with limited lifetime (batteries), [1][2][3] so that increasing environmental and technological concerns motivated the emergence of triboelectric nanogenerators (TENGs) for the direct conversion of mechanical energy (e.g., inhabitants' movements) into useful electricity. [4][5][6][7] However, typical TENGs are often made from non-sustainable highly fluorinated polymers, such as poly(tetrafluoroethylene) (PTFE), obtained from nonrenewable sources and difficult to recycle, and thus inappropriate for large-scale applications in smart homes. 6,[8][9][10][11] Wood, one of the most abundant natural biomaterials on Earth, is convenient, renewable, biocompatible, biodegradable, and has been used as an excellent construction material for thousands of years.…”

Section: Introductionmentioning

confidence: 99%

Functionalized wood with tunable tribopolarity for efficient triboelectric nanogenerators

Sun

Büchele

et al. 2021

Matter

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We describe a functional wood triboelectric nanogenerator (FW-TENG) made by modifying a wood scaffold respectively with ZIF-8 and PDMS. Our approach enables wood with a wide spectrum of triboelectric polarities while preserving its sustainability and aesthetic appearance. We demonstrate the application of our FW-TENG as an energy-harvesting wooden floor or panel, allowing it to power household lamps and other electronic devices when activated by walking or tapping.

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Section: Introductionmentioning

confidence: 99%

Functionalized wood with tunable tribopolarity for efficient triboelectric nanogenerators

Sun

Büchele

et al. 2021

Matter

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“…Inherently, such a technique is a sort of tapping via induction coupling. However, there are concerns with such schemes, aside from a few anecdotal accounts of electricity theft by induction coupling, e.g., [13][14][15]. Primarily, due to the massive amount of copper necessary to build a suitably big coil, a return on investment is often improbable [16].…”

Section: A Outright Theftmentioning

confidence: 99%

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

et al. 2021

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The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

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“…In recent years, triboelectric nanogenerators (TENG) with the benefits of high power density, light weight, etc., have been developed as a green energy technique for energy harvesting under various application scenarios [12][13][14]. TENG technology advancements have resulted in a wide range of applications, including self-powered biomedical devices (e.g., electronic skins, mechanical communication systems, and wearable systems), self-powered engineering devices (e.g., temperature fluctuation and structural vibration sensors), and various other sensing devices (e.g., motion vector sensing, wearable sensors, driving monitoring, fish bladder film-based position monitoring, self-functional healthcare and monitoring socks, and human-machine interfacing) [15][16][17][18][19]. Triboelectric nanogenerators (TENG) [20] were proposed in 2012 as a new kind of energy harvesting approach.…”

Section: Introductionmentioning

confidence: 99%

Rolling Spherical Triboelectric Nanogenerators (RS-TENG) under Low-Frequency Ocean Wave Action

Wang

Nazar

Wang

et al. 2021

JMSE

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Triboelectric nanogenerators (TENG), which convert mechanical energy (such as ocean waves) from the surrounding environment into electrical energy, have been identified as a green energy alternative for addressing the environmental issues resulting from the use of traditional energy resources. In this experimental design, we propose rolling spherical triboelectric nanogenerators (RS-TENG) for collecting energy from low-frequency ocean wave action. Copper and aluminum were used to create a spherical frame which functions as the electrode. In addition, different sizes of spherical dielectric (SD1, SD2, SD3, and SD4) were developed in order to compare the dielectric effect on output performance. This design places several electrodes on each side of the spherical structure such that the dielectric layers are able to move with the slightest oscillation and generate electrical energy. The performance of the RS-TENG was experimentally investigated, with the results indicating that the spherical dielectrics significantly impact energy harvesting performance. On the other hand, the triboelectric materials (i.e., copper and aluminum) play a less important role. The copper RS-TENG with the largest spherical dielectrics is the most efficient structure, with a maximum output of 12.75 V in open-circuit and a peak power of approximately 455 nW.

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Green energy from working surfaces: a contact electrification–enabled data theft protection and monitoring smart table

Cited by 24 publications

References 38 publications

Functionalized wood with tunable tribopolarity for efficient triboelectric nanogenerators

Functionalized wood with tunable tribopolarity for efficient triboelectric nanogenerators

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Rolling Spherical Triboelectric Nanogenerators (RS-TENG) under Low-Frequency Ocean Wave Action

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