An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions.

The Quantitative Ion Character-Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (qmax) of multi-walled carbon for heavy metals. The experimental values of qmax for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with qmax values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method-orthogonal correction signal-PLS model, with RMSEC = 0.733, r2c = 0.999 and r2p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.

Simultaneous spectrophotometric determination of trace amount of polycyclic aromatic hydrocarbons in water samples after magnetic solid-phase extraction by using projection pursuit regression.

Magnetic solid-phase extraction based on coated nano-magnets Fe(3)O(4) was applied for the preconcentration of four polycyclic aromatic hydrocarbons (PAHs; anthracene, phenanthrene, fluorine, and pyrene) in environmental water samples prior to simultaneous spectrophotometric determination using multivariate calibration method. Magnetic nanoparticles, carrying target metals, were easily separated from the aqueous solution by applying an external magnetic field so, no filtration or centrifugation was necessary. After elution of the adsorbed PAHs, the concentration of PAHs was determined spectrophotometrically with the aid of a new and efficient multivariate spectral analysis base on principal component analysis-projection pursuit regression, without separation of analytes. The obtained results revealed that using projection pursuit regression as a flexible modeling approach improves the predictive quality of the developed models compared with partial least squares and least squares support vector machine methods. The method was used to determine four PAHs in environmental water samples.

Simultaneous spectrophotometric determination of trace amounts of uranium, thorium, and zirconium using the partial least squares method after their preconcentration by alpha-benzoin oxime modified Amberlite XAD-2000 resin.

A new solid phase extraction method for separation and preconcentration of trace amounts of uranium, thorium, and zirconium in water samples is proposed. The procedure is based on the adsorption of U(VI), Th(IV) and Zr(IV) ions on a column of Amberlite XAD-2000 resin loaded with alpha-benzoin oxime prior to their simultaneous spectrophotometric determination with Arsenazo III using orthogonal signal correction partial least squares method. The enrichment factor for preconcentration of uranium, thorium, and zirconium was found to be 100. The detection limits for U(VI), Th(IV) and Zr(IV) were 0.50, 0.54, and 0.48microgL(-1), respectively. The precision of the method, evaluated as the relative standard deviation obtained by analyzing a series of 10 replicates, was below 4% for all elements. The practical applicability of the developed sorbent was examined using synthetic seawater, natural waters and ceramic samples.