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

作　　者：Chenyu Xing Gaoyu Chen Xia Zhu Jiakun An Jianchun Bao Xuan Wang Xiuqing Zhou Xiuli Du Xiangxing Xu 

机构地区：[1]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 [2]School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China [3]State Key Laboratory of Coordination Chemistry,School of Chemistry and Chemical Engineering,Nanjing University,Nanjing 210093,China

出　　处：《Nano Research》2024年第3期1984-1989,共6页纳米研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.22175095).

摘　　要：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(λ_(s))and the longest wavelength(λ_(l))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.

关 键 词：carbon dots(CDs) synthesis photoluminescence(PL) machine learning(ML) PL prediction 

分 类 号：O62[理学—有机化学]