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The importance of proper processing and evaluation of experimental data in voltammetry

Experiments for the physico-chemical characterization of materials and heterogeneous processes are carried out using different methods. They are often close to their sensitivity limits because of the nature of the experiment. A degree of the signal reproducibility directly depends on the signal/noise ratio. Thus, it is important that the processing and interpretation of the experimental data are well defined and uniformly accomplished. Sigmoidal and peak-shaped signals are usually the results of the voltammetric measurements of the reduction and oxidation of reactants. In data treatment one is encountered with problems of noise elimination, baseline correction, readout of characteristic values, such as height, position, halfwidth and surface of a peak determination and resolution of the overlapped peaks. All these interventions are applied to the primary curves, which mainly consist of few hundreds of points. Secondary curves, constructed from the readout of the characteristic values from the series of primary curves, consist of up to few tens of points. They are fitted to the selected models. The parameters of fitting and their confidence limits are influenced by the treatment of the primary curves, so before final interpretation of the results, repeated processing which comprises the change of model is often encountered. The broader knowledge of the system investigated might however be helpful and deciding. The treatment of voltammetric signals sets an example for an appropriate application of the chosen steps in the procedure, which affect the final results and conclusions.

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