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Application of Convolutional Neural Networks for Creation of Photoluminescent Carbon Nanosensor for Heavy Metals Detection

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Abstract

The paper presents results of the use of convolutional neural networks for the development of a multimodal photoluminescent nanosensor based on carbon dots (CD) for simultaneous measurement of the number of parameters of multicomponent liquid media. It is shown that using 2D convolutional neural networks allows to determine the concentrations of heavy metal cations Cu2+, Ni2+, Cr3+, \({\text{NO}}_{3}^{ - }\) anions and pH value of aqueous solutions with a mean absolute error of 0.29, 0.96, 0.22, 1.82 and 0.05 mM, respectively. The resulting errors satisfy the needs of monitoring the composition of technological and industrial waters.

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Funding

This work was supported by the Russian Science Foundation, project no. 22-12-00138, https://rscf.ru/en/project/22-12-00138/. Some of the experimental results used in this work were obtained using a FTIR spectrometer purchased under the Development Program of Moscow State University (Agreement no. 65, 04.10.2021).

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Authors and Affiliations

  1. Department of Physics, Moscow State University, 119991, Moscow, Russia

    G. N. Chugreeva, O. E. Sarmanova, K. A. Laptinskiy, S. A. Burikov & T. A. Dolenko

  2. Skobeltsyn Institute of Nuclear Physics, Moscow State University, 119991, Moscow, Russia

    K. A. Laptinskiy

Authors
  1. G. N. Chugreeva
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  2. O. E. Sarmanova
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  3. K. A. Laptinskiy
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  4. S. A. Burikov
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  5. T. A. Dolenko
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Corresponding author

Correspondence to G. N. Chugreeva.

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Chugreeva, G.N., Sarmanova, O.E., Laptinskiy, K.A. et al. Application of Convolutional Neural Networks for Creation of Photoluminescent Carbon Nanosensor for Heavy Metals Detection. Opt. Mem. Neural Networks 32 (Suppl 2), S244–S251 (2023). https://doi.org/10.3103/S1060992X23060036

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  • DOI: https://doi.org/10.3103/S1060992X23060036

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