Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal IonsстатьяИсследовательская статья
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Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.
Аннотация: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 Cr3+, Ni2+, Cu2+, and nitrate anions NO3− 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 Cr3+, by 41% for Ni2+, by 62% for Cu2+, and by 48% for NO3−.