The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry

Abstract

Not long ago, carbon quantum dots (CQDs) came into view as a revolutionary class of materials, propelling advancements in water remediation and electrochemical technology. This comprehensive review explores the cutting-edge developments in CQDs-based materials and their applications, addressing critical challenges in water treatment and electrochemical processes. Synthesized as ultra-tiny, dispersed particles with dimensions less than 10 nm, CQDs exhibit remarkable optical properties, including adjustable fluorescence emission across various colors. With a surge in published scientific articles, CQDs have garnered significant attention, offering potential solutions in heavy metal sensing, remediation, and electrocatalytic hydrogen evolution reactions (HER). The review highlights the high sensitivity of CQDs as fluorescent sensors, detecting contaminants in water with limits of detection down to femtomolar concentrations. Moreover, CQDs demonstrate excellent adsorptive capabilities for heavy metal removal, surpassing traditional adsorbents in terms of removal efficiency. Furthermore, CQDs serve as promising electrocatalysts, enhancing reaction kinetics and enabling efficient water splitting for clean energy generation. Furthermore, this review emphasizes the importance of machine learning in advancing CQDs-based materials, supported by case studies and examples that illustrate how machine learning techniques optimize CQDs synthesis, enhance their properties, and broaden their applications. However, challenges remain in the precise synthesis of CQDs, scalability of production processes, and understanding the interactions between CQDs and pollutants. Overcoming these challenges will unlock the full potential of CQDs-based materials, leading to sustainable and efficient solutions in water control and electrochemical processes.