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Optical Memory and Neural Networks

Erschienen in:

01.12.2023

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

verfasst von: G. N. Chugreeva, O. E. Sarmanova, K. A. Laptinskiy, S. A. Burikov, T. A. Dolenko

Erschienen in: Optical Memory and Neural Networks | Sonderheft 2/2023

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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.

Vorheriger Artikel Strategy of Incremental Learning on a Compartmental Spiking Neuron Model

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Titel: Application of Convolutional Neural Networks for Creation of Photoluminescent Carbon Nanosensor for Heavy Metals Detection
verfasst von: G. N. Chugreeva
O. E. Sarmanova
K. A. Laptinskiy
S. A. Burikov
T. A. Dolenko
Publikationsdatum: 01.12.2023
Verlag: Pleiades Publishing
Erschienen in: Optical Memory and Neural Networks / Ausgabe Sonderheft 2/2023
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X23060036

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