Exploiting deep learning for predictable carbon dot design-Reference-Cited by-同舟云学术

Exploiting deep learning for predictable carbon dot design

Published:2021 Issue:4 Volume:57 Page:532-535
ISSN:1359-7345
Container-title:Chemical Communications
language:en
Short-container-title:Chem. Commun.

Author:

Wang Xiao-Yuan¹²³,Chen Bin-Bin¹²³,Zhang Jie¹²³,Zhou Ze-Rui¹²³,Lv Jian¹²³,Geng Xiao-Peng¹²³,Qian Ruo-Can¹²³^ORCID

Affiliation:

1. Key Laboratory for Advanced Materials School of Chemistry & Molecular Engineering, East China University of Science and Technology

2. Shanghai

3. P. R. China

Abstract

This work developed a multi-layer deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation.

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Metals and Alloys,Surfaces, Coatings and Films,General Chemistry,Ceramics and Composites,Electronic, Optical and Magnetic Materials,Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CC/D0CC07882D

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