A Neural Network Approach to Zinc and Copper Interferences in Potentiometric Stripping Analysis
Source abstract
Abstract
Zinc and copper are two elements which mutually interfere with each other in stripping analysis. The cause is the formation of a Zn-Cu intermetallic compound in the mercury film, which affects both Cu and Zn analyses. A backward error propagation artificial neural network has been applied in a novel approach for the determination of zinc in the presence of copper using potentiometric stripping analysis. This performed well in determining the correct zinc concentration in the sample when provided with the stripping times of zinc and copper and the copper concentration (determined by shifting the plating potential to a lower value to prevent the zinc being plated onto the mercury film electrode). The unknown zinc concentration was determined following an initial period of network exposure to a set of experimental data, which were used as examples of the required input/output data mapping.
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Research context
OpenAlex topics: Electrochemical Analysis and Applications · Analytical Chemistry and Sensors · Force Microscopy Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Australian Water Quality Centre, Private Mail Bag, Salisbury, South Australia, 5108, Australia.;Analysis and Sensors Group, School of Chemical Technology, University of South Australia, P.O. Box 1, Ingle Farm, South Australia 5095, Australia
Analysis and Sensors Group, School of Chemical Technology, University of South Australia, P.O. Box 1, Ingle Farm, South Australia 5095, Australia
References (29)
Show source-provided references
1. Apey, E. A. · 1989
2. 10.1016/S0022-0728(74)80469-910.1016/S0022-0728(74)80469-9 ↗
3. 10.1007/978-1-4613-4136-9_1410.1007/978-1-4613-4136-9_14 ↗
4. 10.1016/S0003-2670(00)83458-310.1016/S0003-2670(00)83458-3 ↗
5. 10.1039/AN993180032310.1039/AN9931800323 ↗
6. 10.1021/ac00163a01810.1021/ac00163a018 ↗
7. 10.1016/1381-141X(95)80024-W10.1016/1381-141X(95)80024-W ↗
8. 10.1021/ed071p99710.1021/ed071p997 ↗
9. 10.1016/0003-2670(95)00023-S10.1016/0003-2670(95)00023-S ↗
10. 10.1016/0003-2670(95)00091-D10.1016/0003-2670(95)00091-D ↗
11. 10.1016/0003-2670(94)85129-810.1016/0003-2670(94)85129-8 ↗
12. 10.1021/ac00269a02110.1021/ac00269a021 ↗
13. Graham, R. C. · 1993
14. 10.1039/an993180034710.1039/an9931800347 ↗
15. 10.1016/0003-2670(94)80307-210.1016/0003-2670(94)80307-2 ↗
16. 10.1016/0039-9140(85)80195-810.1016/0039-9140(85)80195-8 ↗
17. 10.1016/S0003-2670(01)84627-410.1016/S0003-2670(01)84627-4 ↗
18. Kissinger, P. T. · 1984
19. 10.1039/an993180034710.1039/an9931800347 ↗
20. Massart, D. L. · 1988
21. 10.1039/an988130152910.1039/an9881301529 ↗
22. 10.1039/an989140002510.1039/an9891400025 ↗
23. 10.1016/S0003-2670(00)86038-910.1016/S0003-2670(00)86038-9 ↗
24. Shuman, M. S. · 1984 · 345
25. Skoog, D. A. · 1985
26. 10.1016/0003-2670(92)85193-A10.1016/0003-2670(92)85193-A ↗
27. 10.1177/1045389X920030030310.1177/1045389X9200300303 ↗
28. 10.1021/ed071p40610.1021/ed071p406 ↗
29. 10.1016/S0003-2670(00)80865-X10.1016/S0003-2670(00)80865-X ↗