The third element effect in anodic stripping voltammetry

Addressing the practicalities of anodic stripping voltammetry for heavy metal detection: a tutorial review

We highlight the fundamentals and challenges involved with anodic stripping voltammetry (ASV) using solid electrodes providing a practical guide to anyone wishing to undertake analytical ASV.

Trace lead analysis based on carbon-screen-printed-electrodes modified via 4-carboxy-phenyl diazonium salt electroreduction

This paper describes the use of 4-carboxyphenyl-grafted screen-printed carbon electrodes (4-CP-SPEs) for trace lead analysis. These novel and simple use of electrodes were easily prepared by the electrochemical reduction of the corresponding diazonium salt. Pb detection was then performed by a three-steps method in order to avoid oxygen interference: (i) immersion of the grafted screen-printed electrode (SPE) in the sample and adsorption of Pb(II), (ii) reduction of adsorbed Pb(II) by chronoamperometry (CA), and (iii) oxidation of Pb by Anodic Square Wave Voltammetry (SWV). The reoxidation response was exploited for lead detection and quantification. In order to optimize the analytical responses, the influence of the adsorption medium pH and the adsorption time were investigated. Moreover, an interference study was carried out with Cu(II), Hg(II), Al(III), Mn(II), Zn(II), Cd(II) and no major interference can be expected to quantify Pb(II). The described method provided a limit of detection and a limit of quantification of 1.2 × 10(-9)M and 4.1 × 10(-9)M, respectively. These performances indicate that the 4-CP-SPE could be considered as an efficient tool for environmental analysis.

Artificial neural network processing of stripping analysis responses for identifying and quantifying heavy metals in the presence of intermetallic compound formation

Feed-forward neural networks have been trained to identify and quantify heavy metals in mixtures under conditions where there were significant complications due to intermetallic compound formation. The networks were shown to be capable of (i) correlating voltammetric responses with individual heavy metals in complex mixtures, (ii) determining the relationship between responses and concentrations (including nonlinear relationships due to overlapping peaks and intermetallic compound formation), and (iii) rapidly determining concentrations of individual components from mixtures once trained. Using simulated data, modeled after complex interactions experimentally observed in samples containing Cu and Zn, it has been demonstrated that networks containing two layers of neurons (a nonlinear hidden layer and a linear output layer) can be trained to calculate concentrations under a variety of complicated situations. These include, but are not limited to, cases where the response of the intermetallic compound formed is observed as a shoulder of one of the pure metals and cases where the response of the intermetallic compound formed is not observed in the potential window. In addition, the network described above was trained to simultaneously determine concentrations of four metals (Cu, Pb, Cd, and Zn) in a concentration range where all responses were complicated by intermetallic compound formation (1-500 ppb).