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Research Article

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

Chenyu Xing1, Gaoyu Chen1, Xia Zhu1, Jiakun An1, Jianchun Bao1( ), Xuan Wang2, Xiuqing Zhou2, Xiuli Du2( ), Xiangxing Xu1,3( )
Jiangsu Key Laboratory of Biofunctional Materials, Jiangsu Key Laboratory of New Power Batteries, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, China
School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
State Key Laboratory of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210093, China
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Graphical Abstract

Machine learning (ML) models were used to predict the strongest and longest photoluminescence (PL) peaks (λs and λl) of carbon dots (CDs) prepared from different precursor combinations.

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.

Keywords

carbon dots (CDs)synthesisphotoluminescence (PL)machine learning (ML)PL prediction

Electronic Supplementary Material

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Nano Research
Volume 17 Issue 3,
March 2024
Pages 1984-1989
DOI: 10.1007/s12274-023-5893-6
Cite this article:
Xing C, Chen G, Zhu X, et al. Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning. Nano Research, 2024, 17(3): 1984-1989. https://doi.org/10.1007/s12274-023-5893-6
Topics:
Carbon quantum dots

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Received: 05 May 2023
Revised: 04 June 2023
Accepted: 04 June 2023
Published: 14 July 2023
© Tsinghua University Press 2023
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