Numerical analysis and structural optimization of cylindrical grating-structured triboelectric nanogenerator

Wang, Yudong; Liu, Xinzhi; Zheng, Zhihao; Yin, Yajiang; You, Zheng

doi:10.1016/j.nanoen.2021.106570

Cited by 18 publications

(12 citation statements)

References 34 publications

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“…A theoretical model integrated with an AI optimization model (grey wolf optimization method) was reported by Khorsand et al [11] to characterize the output of the rotary TENGs in the contact-separation mode under various kinematics and geometric conditions. Wang et al [22] presented a supported vector regression (SVR) model to optimize the average power of the cylindrical grating-structured TENGs in the freestanding mode. [22] A DL model was developed to detect and classify the microplastics in the liquid−solid TENG.…”

Section: Current Progress and Limitations Of Analytical Models Of Tengsmentioning

confidence: 99%

“…[35,36] Table 2 summarizes the existing AI models developed for the performance prediction and output data analysis of four modes of TENGs. [12,22,23,[37][38][39][40][41][42][43][44][45]…”

Section: Technical Challenges In Design and Optimization Of Tengsmentioning

confidence: 99%

“…Existing AI models developed for the performance prediction and output data analysis of TENGs. [12,22,23,[37][38][39][40][41][42][43][44][45] Performance prediction Data analysis Contact-separation • K nearest neighbor (KNN) and NN • KNN accuracy of 97.1%…”

Section: From Analytical Modeling To Physics-informed Aimentioning

confidence: 99%

“…We particularly discuss the inverse design strategies in the context of the AI-based methods for expanding the principles from analytical modeling to physicsinformed AI, discovering frictionally conductive compositions, [18] contact-separation, [19] freestanding, [20] and single electrode [21] models. b) Existing AI models to predict the electrical performance of four modes of TENGs including lateral sliding, [12] contact-separation, [11] freestanding mode, [22] and single electrode [23] modes.…”

Section: Introductionmentioning

confidence: 99%

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Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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Section: Current Progress and Limitations Of Analytical Models Of Tengsmentioning

confidence: 99%

“…[35,36] Table 2 summarizes the existing AI models developed for the performance prediction and output data analysis of four modes of TENGs. [12,22,23,[37][38][39][40][41][42][43][44][45]…”

Section: Technical Challenges In Design and Optimization Of Tengsmentioning

confidence: 99%

Section: From Analytical Modeling To Physics-informed Aimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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“…[26][27][28][29][30][31][32] Over the past decade, various studies have been conducted to develop and optimize TENGs, [33][34][35][36][37][38][39][40] showing that their surface morphology, dielectric properties, device structure, and friction layer materials play important roles in determining their performance. [41][42][43][44][45][46] Most of the friction layer materials used to prepare TENGs are synthetic polymers, such as polydimethylsiloxane (PDMS), 47 polyvinylidene diuoride (PVDF), 48 polyethylene terephthalate (PET), 49 polyurethane (PU), 50 polyimide (PI), 51 uorinated ethylene propylene (FEP), 52 and poly-tetrauoroethylene (PTFE), 53 or metals materials such as aluminum (Al) 54 and copper (Cu). 55 However, these materials are associated with disadvantages, such as being high cost, difficult to decompose, requiring complex manufacturing processes, and exhibiting non-biological compatibility.…”

Section: Introductionmentioning

confidence: 99%

Environmentally friendly natural materials for triboelectric nanogenerators: a review

et al. 2023

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Energy Harvesting and Sensing Integrated Woven Structure Kneepad Based on Triboelectric Nanogenerators

Wang

Liu

Wang

et al. 2022

Adv Materials Technologies

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Triboelectric nanogenerators (TENGs) are extensively studied because of their great potential in energy harvesting and sensing. Traditional chemical batteries cannot meet the requirements of wearable electronic products because of their inherent limitations such as large weight, large size, limited endurance, and limited lifespan. To address the issue, the self‐powered energy harvester has attracted considerable interest among researchers. Here, the woven structure TENG consists of flexible silicone mixed with polyvinylidene fluoride as dielectric layers, silver fabric, and conductive tape as electrodes. The electrical output performance of the pattern structure processed by tip design is nearly three times higher. The whole TENG and flexible printed circuit board are encapsulated by polydimethylsiloxane film, reducing inconvenient, and discomfort sensation during wearing. The woven structure TENG can not only harvest energy to power wearable electronic devices but also be used to recognize human motion as the waveforms changing when people are in different states. Taking the knee bending angle data as an example to conduct machine learning, it is able to classify different states of human motion. This work extends the application of the woven TENG as an integrated device for sensing and energy harvesting.

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Numerical analysis and structural optimization of cylindrical grating-structured triboelectric nanogenerator

Cited by 18 publications

References 34 publications

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Environmentally friendly natural materials for triboelectric nanogenerators: a review

Energy Harvesting and Sensing Integrated Woven Structure Kneepad Based on Triboelectric Nanogenerators

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