Issue 4, 2021

Exploiting deep learning for predictable carbon dot design

Abstract

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.

Graphical abstract: Exploiting deep learning for predictable carbon dot design

Supplementary files

Article information

Article type
Communication
Submitted
03 Dec 2020
Accepted
10 Dec 2020
First published
10 Dec 2020

Chem. Commun., 2021,57, 532-535

Exploiting deep learning for predictable carbon dot design

X. Wang, B. Chen, J. Zhang, Z. Zhou, J. Lv, X. Geng and R. Qian, Chem. Commun., 2021, 57, 532 DOI: 10.1039/D0CC07882D

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