文献类型：期刊文献

英文题名：Exploiting deep learning for predictable carbon dot design

作者：Wang, Xiao-Yuan[1];Chen, Bin-Bin[1];Zhang, Jie[1];Zhou, Ze-Rui[1];Lv, Jian[1];Geng, Xiao-Peng[1];Qian, Ruo-Can[1]

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

年份：2021

卷号：57

期号：4

起止页码：532

外文期刊名：CHEMICAL COMMUNICATIONS

收录：;EI(收录号：20210409817673);WOS:【SCI-EXPANDED(收录号:WOS:000608997100021)】；

基金：This research was supported by the National Natural Science Foundation of China (21977031), the National Science and Technology Major Project of China (2018ZX10302205), Shanghai Science and Technology Committee (19ZR1472300), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Carbon - Deep learning - Irradiation - Optical properties

摘要：In this study, we developed a deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation. These results demonstrate the powerful potential of DCNN for guiding the synthesis of CDs.