Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond
- 1School of Physics Science and Information Technology, Liaocheng University, Liaocheng 252000, China
- 2Shandong Jinhengli Mechanical Manufacturing Co., Ltd., Taian 271200, China
- 3Centre of Excellence for Nanotechnology, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India
Abstract
Carbon dots (CDs) have emerged as frontier materials in multidisciplinary research owing to their unique optical properties and physicochemical characteristics. However, issues such as the reliance on trial-and-error experimentation for synthetic preparation and the difficulty in systematically revealing structure-activity relationships persist. In recent years, machine learning (ML) has provided a new paradigm for CD research through its powerful predictive and decision-making capabilities. This review first introduces the fundamental workflow of ML and the operational principles of several representative ML algorithms. It then summarizes the ML applications in CDs, including ML-optimized CD synthesis, ML-assisted detection in CD sensors, ML-based performance prediction, and ML-driven mechanism studies. Finally, the review outlines the future prospects for applications in this field, aiming to further advance the development of nanomaterials science.
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BibTeX
@article{jia2026machinelearningassisted,
title = {{Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond}},
author = {Fengjiao Jia and Hengkai Wang and Deyu Shen and Dandan Sang and Z Z Zhang and Hui Li and Santosh Kumar and Qinglin Wang},
journal = {Molecules},
year = {2026},
month = may,
doi = {10.3390/molecules31101696},
url = {https://doi.org/10.3390/molecules31101696},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond
AU - Fengjiao Jia
AU - Hengkai Wang
AU - Deyu Shen
AU - Dandan Sang
AU - Z Z Zhang
AU - Hui Li
AU - Santosh Kumar
AU - Qinglin Wang
PY - 2026
DA - 2026/05/17
JO - Molecules
DO - 10.3390/molecules31101696
UR - https://doi.org/10.3390/molecules31101696
AB - Carbon dots (CDs) have emerged as frontier materials in multidisciplinary research owing to their unique optical properties and physicochemical characteristics. However, issues such as the reliance on trial-and-error experimentation for synthetic preparation and the difficulty in systematically revealing structure-activity relationships persist. In recent years, machine learning (ML) has provided a new paradigm for CD research through its powerful predictive and decision-making capabilities. This review first introduces the fundamental workflow of ML and the operational principles of several representative ML algorithms. It then summarizes the ML applications in CDs, including ML-optimized CD synthesis, ML-assisted detection in CD sensors, ML-based performance prediction, and ML-driven mechanism studies. Finally, the review outlines the future prospects for applications in this field, aiming to further advance the development of nanomaterials science.
SN - 1420-3049
SN - 1433-1373
ER -