Influence of the type of titration and of data treatment methods on metal complexing parameters determination of single and multi-ligand systems measured by stripping voltammetry

A set of simulated experiments was analysed in order to compare the influence of the titration type and of data treatment methods on the accuracy of metal complexing parameters determination for one- and two-ligand systems. The simulated data corresponded to those obtained by anodic stripping voltam...

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Veröffentlicht in:Analytica chimica acta 2004-03, Vol.505 (2), p.263-275
Hauptverfasser: Garnier, Cédric, Pižeta, Ivanka, Mounier, Stéphane, Benaı̈m, Jean Yves, Branica, Marko
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Sprache:eng
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Zusammenfassung:A set of simulated experiments was analysed in order to compare the influence of the titration type and of data treatment methods on the accuracy of metal complexing parameters determination for one- and two-ligand systems. The simulated data corresponded to those obtained by anodic stripping voltammetry and were chosen to represent experiments in linear, logarithmic and decade titration modes. The values of preset complexing parameters for one- and two-ligand systems were chosen to fit into the expected experimental range. Random noise was added to the data prior to the treatment. Five different data treatments were applied: Chau–Buffle, Ružić–van den Berg and Scatchard linearisations, and non-linear fitting and PROSECE optimisations. The investigation has shown that even in the case of a one-ligand system, logarithmic and decade titrations are much better compared to the linear ones. Linearisation methods are in many cases inferior to those using optimisation algorithms. Random noise has a significant influence on the results of linearisation methods as well. For linearisation methods, in the case of a one-ligand system, high correlation has been found for the confidence interval of the calculated parameters and the difference between the preset and the calculated values. This correlation is proposed to be used as an estimation for the results quality in real experiments. PROSECE is by far superior to other methods in most of the cases due to its flexible and powerful mathematical background. It is highly recommended as a tool for data treatment. Construction of “contour-graphs” enables error prediction of the calculated complexing parameters. PROSECE is proposed as an orientation and valorisation tool in real samples analyses.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2003.10.066