Intelligent Control of the Synthesis of Carbon Dots Used to Create a High-Precision Luminescent Nanosensor of Heavy Metal Ions in Water Using Machine Learning

This article presents the results of solving a “Synthesis-Properties-Application” problem: finding optimal conditions for the hydrothermal synthesis of carbon dots from citric acid and ethylenediamine to create a photoluminescent nanosensor for the heavy metal salts Co(NO3)2 and Cu(NO3)2 in water, providing the highest sensitivity during quantitative analysis. The problem was solved in two stages. In the first stage, machine learning methods were used to estimate the errors in solving the inverse problem of determining the concentrations of Cu2+ and Co2+ ions in water from carbon dot photoluminescence excitation–emission matrix. The average solution error (mean absolute error—MAE) was 0.55 mM for Co2+ and 0.62 mM for Cu2+. In the second stage, optimal conditions for the synthesis of carbon dots with photoluminescence most sensitive to changes in the concentrations of Co2+ and Cu2+ ions in water were determined by analyzing the constructed approximation models of the dependence of the ion concentration determination error on the carbon dot synthesis parameters. The following values were obtained: a precursor ratio of CA:EDA = 1:2, a temperature of 150–155 °C, and a reaction time of 210–230 min. It was shown that carbon dots, effective as nanosensors for determining the concentration of Co2+ and Cu2+ cations in water, are formed mainly in the third stage of hydrothermal synthesis. The conclusions regarding the obtained optimal synthesis conditions were experimentally validated.