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Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning

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Abstract

Carbon dots (CDs) have wide application potentials in optoelectronic devices, biology, medicine, chemical sensors, and quantum techniques due to their excellent fluorescent properties. However, synthesis of CDs with controllable spectrum is challenging because of the diversity of the CD components and structures. In this report, machine learning (ML) algorithms were applied to help the synthesis of CDs with predictable photoluminescence (PL) under the excitation wavelengths of 365 and 532 nm. The combination of precursors was used as the variable. The PL peaks of the strongest intensity (λs) and the longest wavelength (λl) were used as target functions. Among six investigated ML models, the random forest (RF) model showed outstanding performance in the prediction of the PL peaks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 22175095).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Biofunctional Materials, Jiangsu Key Laboratory of New Power Batteries, School of Chemistry and Materials Science, Nan**g Normal University, Nan**g, 210023, China

    Chenyu **ng, Gaoyu Chen, **a Zhu, Jiakun An, Jianchun Bao & **angxing Xu

  2. School of Mathematical Sciences, Nan**g Normal University, Nan**g, 210023, China

    Xuan Wang, **uqing Zhou & **uli Du

  3. State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nan**g University, Nan**g, 210093, China

    **angxing Xu

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  1. Chenyu **ng
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  2. Gaoyu Chen
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  3. **a Zhu
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  4. Jiakun An
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  5. Jianchun Bao
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  6. Xuan Wang
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  7. **uqing Zhou
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  8. **uli Du
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  9. **angxing Xu
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Correspondence to Jianchun Bao, **uli Du or **angxing Xu.

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**ng, C., Chen, G., Zhu, X. et al. Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning. Nano Res. 17, 1984–1989 (2024). https://doi.org/10.1007/s12274-023-5893-6

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  • DOI: https://doi.org/10.1007/s12274-023-5893-6

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