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APPLICATION OF CHITOSAN-ZINC OXIDE NANOPARTICLES FOR LEAD EXTRACTION FROM WATER SAMPLES BY THE COMBINED OF ANT COLONY OPTIMIZATION-ARTIFICIAL NEURAL NETWORK

Abstract

Сhitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multi-layer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 µg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.

About the Authors

M. . Khajeh
University of Zabol
Russian Federation


A. . Pourkarami
Zabol University of Medical Science
Russian Federation


E. . Arefnejad
University of Zabol
Russian Federation


M. . Bohlooli
University of Zabol
Russian Federation


A. . Khatibi
Institute of Biochemistry and Biophysics, University of Tehran
Russian Federation


M. . Ghaffari-Moghaddam
University of Zabol
Russian Federation


S. . Zareian-Jahromi
University of Sistan and Baluchestan
Russian Federation


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Review

For citations:


Khajeh M., Pourkarami A., Arefnejad E., Bohlooli M., Khatibi A., Ghaffari-Moghaddam M., Zareian-Jahromi S. APPLICATION OF CHITOSAN-ZINC OXIDE NANOPARTICLES FOR LEAD EXTRACTION FROM WATER SAMPLES BY THE COMBINED OF ANT COLONY OPTIMIZATION-ARTIFICIAL NEURAL NETWORK. Zhurnal Prikladnoii Spektroskopii. 2017;84(4):672(1)-672(9). (In Russ.)

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ISSN 0514-7506 (Print)