Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design
© 2023 Wiley-VCH GmbH.
| Publié dans: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 5 vom: 08. Feb., Seite e2308505 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | Advanced materials (Deerfield Beach, Fla.) |
| Sujets: | Journal Article physics-informed artificial intelligence strategic inverse design triboelectric nanogenerators |
| Résumé: | © 2023 Wiley-VCH GmbH. 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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| Description: | Date Revised 01.02.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202308505 |