Development of a Multimodal Photoluminescent Carbon Nanosensor for Metal Ions in Water Using Artificial Neural Networks

Abstract

The study presents a novel approach to the development of a photoluminescent multimodal carbon dots-based nanosensor for determining the concentration of Cu\({}^{2+}\), Ni\({}^{2+}\), Co\({}^{2+}\), Al\({}^{3+}\), Cr\({}^{3+}\) and NO\({}^{-}_{3}\) ions in water using machine learning methods. The results show that it is possible to determine the type and concentration of each of these ions in multicomponent aqueous media from the photoluminescence spectra of carbon dots. The mean absolute error of simultaneous determination of Cu\({}^{2+}\), Ni\({}^{2+}\), Co\({}^{2+}\), Al\({}^{3+}\), Cr\({}^{3+}\) cations and NO\({}^{-}_{3}\) anion concentration was 0.85, 0.97, 0.67, 0.81, 0.26, and 2.03 mM, respectively. The accuracy of the developed nanosensor fully meets the requirements for wastewater and process water composition control. The developed nanosensor can not only simultaneously determine the concentration of each of the 6 ions, but also provides real-time remote determination of ion concentrations.