Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning
- DOI
- 10.1038/s41467-025-63653-2
- Published
- 2025-09-01
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-025-63653-2,
title = {Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning},
author = {Yu Lan and Guang-Song Zheng and Run-Wei Song and Jing-Nan Hao and Jia-Lu Liu and Cheng-Long Shen and Jin-Yang Zhu and Sheng Cao and Jia-Long Zhao and Qing Lou and Chong-Xin Shan},
year = {2025},
journal = {Nature Communications},
doi = {10.1038/s41467-025-63653-2},
url = {https://doi.org/10.1038/s41467-025-63653-2}
}RIS
TY - JOUR TI - Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning AU - Yu Lan AU - Guang-Song Zheng AU - Run-Wei Song AU - Jing-Nan Hao AU - Jia-Lu Liu AU - Cheng-Long Shen AU - Jin-Yang Zhu AU - Sheng Cao AU - Jia-Long Zhao AU - Qing Lou AU - Chong-Xin Shan PY - 2025 JO - Nature Communications DO - 10.1038/s41467-025-63653-2 UR - https://doi.org/10.1038/s41467-025-63653-2 ER -
APA
Lan, Y., Zheng, G., Song, R., Hao, J., Liu, J., Shen, C., Zhu, J., Cao, S., Zhao, J., Lou, Q., & Shan, C. (2025). Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning. Nature Communications. https://doi.org/10.1038/s41467-025-63653-2
Source records
- crossref · retrieved 2026-09-26T16:48:09.482Z