Machine learning methods for designing a carbon dot based photoluminescent multimodal nanosensor.

This study addresses the problem of simultaneous determination of metal cations and nitrate anions concentrations in multicomponent aqueous solutions. To solve this task, a photoluminescent nanosensor based on carbon dots synthesized by the hydrothermal method from citric acid and ethylenediamine is developed. A key novel feature of the developed nanosensor is its multimodality, i.e. the ability to simultaneously determine (by the same photoluminescent spectrum) the concentrations of all ions under consideration. Various representations of the photoluminescent spectrum of carbon dots added to the studied solution are used as the source of information. To determine the target concentrations from the photoluminescent spectra under consideration, machine learning methods are used: neural networks of the multilayer perceptron type, convolutional neural networks, Kolmogorov-Arnold neural networks, gradient boosting, and linear regression. The use of the transfer learning technique for neural networks in the transition from solving a 6-parameter problem to solving a 7-parameter problem is considered. It was shown that the best results are achieved using convolutional neural networks. (The mean absolute errors of simultaneous determination of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] , cations and [Formula: see text] anion concentrations were 0.68, 1.05, 0.43, 1.38, 1.08, 0.32 and 2.43 mM, respectively.) Such precision meets the requirements for determining the concentration of ions in wastewater and process water. Transfer learning allows reducing the computational cost of the solution. Kolmogorov-Arnold networks can provide a visual interpretation of the resulting model.