Implementing neural network approach to create carbon-based optical nanosensor of heavy metal ions in liquid media.

The present study is devoted to the creation of multifunctional optical carbon dots-based nanosensor to simultaneously measure concentrations of metal ions (Cu2+, Ni2+, Cr3+) and NO3- anion in liquid media. Such nanosensor operates on the basis of its fluorescence (FL) change under the influence of ions in the medium. However, the absence of analytical model, describing CD FL mechanism, the superposition of various luminescence quenching mechanisms during the interaction of carbon dots (CD) with cations, hampers the usage of classical approaches to solve this inverse multiparametric spectroscopic problem. To solve it neural networks were used that analyzed complex fluorescence signal from CD aqueous suspensions comprising Cu2+, Ni2+, Cr3+, NO3- ions in the concentration range from 0 to 4.95 mM. The following neural network architectures ensured optical spectroscopy inverse problem solution: multilayer perceptrons, 1D and 2D convolutional neural networks. The developed sensor enables simultaneous determination of the concentrations of heavy metal ions Cu2+, Ni2+, Cr3+ with a root mean squared error of 0.28 mM, 0.79 mM, 0.24 mM respectively. Based on the data given in the literature we can assert that the accuracy of the studied nanosensor satisfies the needs of monitoring the composition of waste and technological water. The developed nanosensor has a unique multimodality: with the simplicity of the synthesis protocol the sensor enables simultaneous determination of three heavy metal ions concentrations, while analogues are being developed mainly to measure the concentration of one (in rare cases two) heavy metal ions.

Development of the fluorescent carbon nanosensor for pH and temperature of liquid media with artificial neural networks.

The present study is devoted to the creation of optical nanosensors for pH and temperature of liquid media based on carbon dots (CD) prepared via hydrothermal synthesis. The application of artificial neural networks to the CD fluorescence spectra database provided simultaneous determination of pH and ambient temperature values with an accuracy of 0.005 pH units and 0.67 °C, respectively. The obtained results are unique since they indicate the possibility of creating a multifunctional CD-based nanosensor that operates in a wide temperature range (22-81 °C) and provides an accuracy of pH determination exceeding the accuracy of nanoscale analogs by an order of magnitude.