Split-plot design optimization for trace determination of lead by anodic stripping voltammetry in a homogeneous ternary solvent system

A split-plot design has been used to simultaneously optimize reagent conditions and solvent medium for Ph2+ determination by anodic stripping voltammetry (ASV). Three mixture components, N,N-dimethylformamide (DMF), ethanol and water, and two process variable levels, ammonium acetate (supporting electrolyte) and hydrochloric acid concentrations, were varied. The calculations of main-plot, subplot and main-subplot interaction ANOVA sums of squares for regression and lack of fit are illustrated. These values are shown to be useful for model development. Six different models were evaluated. The bilinear-special cubic model has only a very slight lack of fit and is preferred. Standard error estimates were calculated using a method described in the literature by Cornell, and approximate critical values for t-tests on the model coefficients were investigated. Normal probability graphs seem appropriate for this analysis. The significant terms in this model are capable of describing how the mixture response surfaces change as process level conditions are varied. Optimized mixture proportions for each factorial design level are determined. Optimum conditions in the ( - -) main-plot quadrant for the determination of lead by ASV in the homogeneous ternary solvent system were achieved with a solvent composition of 8.0 g of N,N-dimethylformamide (DMF), 7.0 g of ethanol and 5.0 g of water, corresponding to 40% m/m of DMF, 35% n/m of ethanol and 25% m/m of water. Water is essential since the sample and the buffer (0.1 mol kg(-1) ammonium acetate containing 0.00880 mol kg(-1) HCl) are added to the system as aqueous solutions. The other three main-plot quadrants presented optimum analytical signals for 95% aqueous solutions. The split-plot design seems to be especially appropriate for simultaneously optimizing process and mixture variables of this chemical system facilitating operational procedures by means of block randomization. (C) 2004 Elsevier B.V All rights reserved.