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Journal of Intelligent Material Systems and Structures
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A Neural Network Approach to Zinc and Copper Interferences in Potentiometric Stripping Analysis

Christopher W. K. Chow

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

David E. Davey

Dennis E. Mulcahy

Analysis and Sensors Group, School of Chemical Technology, University of South Australia, P.O. Box 1, Ingle Farm, South Australia 5095, Australia

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.

Journal of Intelligent Material Systems and Structures, Vol. 8, No. 2, 177-183 (1997)
DOI: 10.1177/1045389X9700800208
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