Abstract
In this study, to create a carbon dots-based multimodal nanosensor of metal ions, a new approach to solving the inverse problem of fluorescence spectroscopy is presented. The problem is to simultaneously determine the concentration of heavy metal ions Cr\({}^{3+}\), Ni\({}^{2+}\), Cu\({}^{2+}\), and nitrate anions NO\({}^{-}_{3}\) in water by carbon dots (CDs) fluorescence spectra. A method of spectral data augmentation is proposed. It is based on the generation of excitation-emission matrices of CDs fluorescence from the noise vector using variational autoencoders and further determination of ion concentration corresponding to the generated matrices with convolutional neural networks. Implementing the proposed approach allowed reducing the mean absolute error in determining the concentration of ions by 60\(\%\) for Cr\({}^{3+}\), by 41\(\%\) for Ni\({}^{2+}\), by 62\(\%\) for Cu\({}^{2+}\), and by 48\(\%\) for NO\({}^{-}_{3}\).
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Funding
This study has been conducted at the expense of the grant of the Russian Science Foundation no. 22-12-00138, https://rscf.ru/en/project/22-12-00138/. The contribution of O.E. Sarmanova (programming and training of neural networks) was supported by the Foundation for the Development of Theoretical Physics and Mathematics ‘‘Basis’’ (project no. 19-2-6-6-1).
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Sarmanova, O.E., Chugreeva, G.N., Laptinskiy, K.A. et al. Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions. Moscow Univ. Phys. 78 (Suppl 1), S202–S209 (2023). https://doi.org/10.3103/S0027134923070287
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DOI: https://doi.org/10.3103/S0027134923070287