文献类型：期刊文献

英文题名：Customized Carbon Dots with Predictable Optical Properties Synthesized at Room Temperature Guided by Machine Learning

作者：Hong, Qin[1,2];Wang, Xiao-Yuan[1,2];Gao, Ya-Ting[1,2];Lv, Jian[1,2];Chen, Bin-Bin[1,2];Li, Da-Wei[1,2];Qian, Ruo-Can[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Mat, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：34

期号：3

起止页码：998

外文期刊名：CHEMISTRY OF MATERIALS

收录：;EI(收录号：20220611586171);WOS:【SCI-EXPANDED(收录号:WOS:000763584500010)】；

基金：This research was supported by the National Natural Science Foundation of China (21788102, 21977031, 22176058), the Shanghai Science and Technology Committee (19ZR1472300), and the Fundamental Research Funds for the Central Universities. The authors thank Dr. Bo-Hao Yu at the Research Center of Analysis and Test of East China University of Science and Technology for help on confocal FL imaging analysis and FL spectrophotometer analysis.

语种：英文

外文关键词：Film preparation - Fluorescence imaging - Polyvinyl alcohols - Room temperature - Machine learning - Carbon - Drug delivery - Optical properties

摘要：Fluorescent carbon dots (CDs) have been increasingly used in fluorescence detection and imaging based on their tunable fluorescence (FL) and resistance to photobleaching. However, the fast and reliable design of fluorescent CDs with specific optical properties involves a number of factors, such as the concentration of precursors, reaction time, and solvents. Therefore, it is usually considered difficult to design CDs with favorable optical properties. Herein, we report an extreme gradient boosting (XGBoost) model for guiding the fabrication of CDs with high FL intensity and tunable emission from p-benzoquinone (PBQ) and ethylenediamine (EDA) in different solvents at room temperature. Among a variety of studied machine learning models, XGBoost shows the best performance in the field of material synthesis, with a prediction coefficient of determination (R-2) higher than 0.96. The XGBoost model can effectively predict the optical properties of CDs, including the maximum FL intensity and emission centers. Guided by the XGBoost model, various green or blue fluorescent CDs with adjustable emission centers and solubility properties are designed and fabricated accurately. These CDs are successfully applied for Fe3+ detection, sustained drug release, whole-cell imaging, and poly(vinyl alcohol) (PVA) film preparation. These results suggest the great potential of the combination of machine learning and CD synthesis as an effective strategy to help researchers realize accurate selection of reasonable CDs with individual customized properties to achieve different goals.