Machine learning-driven approaches for synthesizing carbon dots and their applications in photoelectrochemical sensors

Carbon dots (CDs) have been a subject of great interest among researchers due to their diverse physicochemical properties and numerous advantageous attributes such as good biocompatibility, unique optical properties, low cost, eco-friendliness, abundant functional groups (e.g., amino, hydroxyl, and carboxyl) high stability, and excellent electron mobility. With the rapid advancement of data-driven technologies, machine learning (ML) has gained significant attention as a primary and indispensable tool in different applications in numerous research fields, including the monitoring of chemical reactions. By utilizing machine learning algorithms, the properties of carbon dots can be enhanced, such as fluorescence, stability, and electrocatalytic activity, as well as optimizing the synthesis process. Moreover, machine learning can be utilized to screen carbon dot precursors and predict their properties, providing various advantages in developing carbon dots with superior properties. As a result, machine learning offers numerous benefits in carbon dots synthesis, which has the potential to impact various fields. Photoelectrochemical sensors are a type of chemical sensor that use light to generate a photocurrent, which is then used to detect the presence of a target analyte. These sensors have gained significant attention due to their high sensitivity, selectivity, and low cost, making them a promising tool for a variety of applications in fields such as environmental monitoring and biomedical sensing. Due to their fascinating electronic and photonic properties, CQDs have gained considerable attention in the development of photoelectrochemical sensors. This review article provides an overview of recent advancements in the machine learning synthesis of CQDs and their applications in constructing photoelectrochemical sensors.