| Autor: |
Ma Wenzhuang, Chen Wei, Li Degui, Liu Yue, Yin Juhang, Tu Chunzhi, Xia Yunlong, Shen Gefei, Zhou Peiheng, Deng Longjiang, Zhang Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Nanophotonics, Vol 12, Iss 18, Pp 3589-3601 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2192-8614 |
| DOI: |
10.1515/nanoph-2023-0291 |
| Popis: |
The selective broadband absorption of solar radiation plays a crucial role in applying solar energy. However, despite being a decade-old technology, the rapid and precise designs of selective absorbers spanning from the solar spectrum to the infrared region remain a significant challenge. This work develops a high-performance design paradigm that combines deep learning and multi-objective double annealing algorithms to optimize multilayer nanostructures for maximizing solar spectral absorption and minimum infrared radiation. Based on deep learning design, we experimentally fabricate the designed absorber and demonstrate its photothermal effect under sunlight. The absorber exhibits exceptional absorption in the solar spectrum (calculated/measured = 0.98/0.94) and low average emissivity in the infrared region (calculated/measured = 0.08/0.19). This absorber has the potential to result in annual energy savings of up to 1743 kW h/m2 in areas with abundant solar radiation resources. Our study opens a powerful design method to study solar-thermal energy harvesting and manipulation, which will facilitate for their broad applications in other engineering applications. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
