Utilizing machine learning to expedite the fabrication and biological application of carbon dots

Abstract

As a novel type of nanomaterial, carbon dots (CDs) are widely used in biology owing to their optical property, biocompatibility, and intrinsic theranostic properties. Taking advantage of these features, the CDs serve as color agents, fluorescence probes, and anti-cancer drugs. Machine learning (ML) has progressed dramatically, especially for widespread use in the biological field. In this review, we introduce the ML workflow and the leading models in the process and then demonstrate the application of CDs in bioimaging, biosensing, and cancer treatment. Next, we generalize the use in the development of CDs’ combination of ML in complementary aspects. Finally, we briefly summarize the challenges and expectations for the future. This review provides new thoughts and guidance for CDs on the application and integration of machine learning.