Machine learning guided microwave-assisted quantum dot synthesis and an indication of residual H2O2 in human teeth

Abstract

The current preparation methods of carbon quantum dots (CDs) involve many reaction parameters, which leads to many possibilities in the synthesis processes and high uncertainty of the resultant production performance. Recently, machine learning (ML) methods have shown great potential in correlating the selected features in many applications, which can help understand the relevant structure–function relationships of CDs and discover better synthesis recipes as well. In this work, we employ the ML approach to guide the blue CD synthesis in microwave systems. After optimizing the synthesis parameters and conditions, the quantum yield (QY) increases to about 200% higher than the average value of the prepared samples without ML guidance. The obtained CDs are applied as fluorescent probes to monitor hydrogen peroxide (H₂O₂) in human teeth. The CD probe exhibits a linear relationship with the concentration of H₂O₂ ranging from 0 to 1.1 M with a lower detection limit of 0.12 M, which can effectively detect the residual H₂O₂ after bleaching teeth. This work shows that the adopted ML methods have considerable advantages in guiding the synthesis of high-quality CDs, which could accelerate the development of other novel functional materials in energy, biomedical, and environmental remediation applications.