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Sensors 2015, 15(1), 592-610; doi:10.3390/s150100592

Simultaneous Automatic Electrochemical Detection of Zinc, Cadmium, Copper and Lead Ions in Environmental Samples Using a Thin-Film Mercury Electrode and an Artificial Neural Network

1
Department of Chemistry and Biochemistry, Faculty of Agronomy, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
2
Central European Institute of Technology, Brno University of Technology, Technicka 3058/10, CZ-616 00 Brno, Czech Republic
3
Karel Englis College, Sujanovo nam. 356/1, Brno CZ-602 00, Czech Republic
*
Author to whom correspondence should be addressed.
Received: 23 October 2014 / Accepted: 11 December 2014 / Published: 30 December 2014
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Abstract

In this study a device for automatic electrochemical analysis was designed. A three electrodes detection system was attached to a positioning device, which enabled us to move the electrode system from one well to another of a microtitre plate. Disposable carbon tip electrodes were used for Cd(II), Cu(II) and Pb(II) ion quantification, while Zn(II) did not give signal in this electrode configuration. In order to detect all mentioned heavy metals simultaneously, thin-film mercury electrodes (TFME) were fabricated by electrodeposition of mercury on the surface of carbon tips. In comparison with bare electrodes the TMFEs had lower detection limits and better sensitivity. In addition to pure aqueous heavy metal solutions, the assay was also performed on mineralized rock samples, artificial blood plasma samples and samples of chicken embryo organs treated with cadmium. An artificial neural network was created to evaluate the concentrations of the mentioned heavy metals correctly in mixture samples and an excellent fit was observed (R2 = 0.9933). View Full-Text
Keywords: automation; electrochemical detection; artificial neuronal network; robotic device; metal ions; environmental analysis automation; electrochemical detection; artificial neuronal network; robotic device; metal ions; environmental analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kudr, J.; Nguyen, H.V.; Gumulec, J.; Nejdl, L.; Blazkova, I.; Ruttkay-Nedecky, B.; Hynek, D.; Kynicky, J.; Adam, V.; Kizek, R. Simultaneous Automatic Electrochemical Detection of Zinc, Cadmium, Copper and Lead Ions in Environmental Samples Using a Thin-Film Mercury Electrode and an Artificial Neural Network. Sensors 2015, 15, 592-610.

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