High-efficiency synthesis of red carbon dots using machine learning.

Due to their excellent optical properties, red carbon dots (CDs) have been widely used in cell imaging and biomedical therapy. However, the efficiency of red CD synthesis is deficient, and the synthesis cost is high. Here, we propose an efficient synthesis method based on machine learning to assist researchers in synthesizing red fluorescent CDs. This strategy can quickly and efficiently predict the predesigned conditions of CD synthesis. It avoids invalid synthetic experiments and improves the efficiency of red CD synthesis.