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|a 10.1002/adma.202308505
|2 doi
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Jiao, Pengcheng
|e verfasserin
|4 aut
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| 245 |
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|a Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 01.02.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 Wiley-VCH GmbH.
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|a 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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|a Journal Article
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| 650 |
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|a physics-informed artificial intelligence
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| 650 |
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4 |
|a strategic inverse design
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| 650 |
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4 |
|a triboelectric nanogenerators
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| 700 |
1 |
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|a Wang, Zhong Lin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Alavi, Amir H
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 5 vom: 01. Feb., Seite e2308505
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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|g volume:36
|g year:2024
|g number:5
|g day:01
|g month:02
|g pages:e2308505
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202308505
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