Ligandless cloud point extraction of trace amounts of palladium and rhodium in road dust samples using Span 80 prior to their determination by flame atomic absorption spectrometry

In this study, a procedure is developed for cloud point extraction of Pd(II) and Rh(III) ions in aqueous solution using Span 80 (non-ionic surfactant) prior to their determination by flame atomic absorption spectroscopy. This method is based on the extraction of Pd(II) and Rh(III) ions at a pH of 10 using Span 80 with no chelating agent. We investigated the effect of various parameters on the recovery of the analyte ions, including pH, equilibration temperature and time, concentration of Span 80, and ionic strength. Under the best experimental conditions, the limits of detection based on 3Sb for Pd(II) and Rh(III) ions were 1.3 and 1.2 ng mL-1, respectively. Seven replicate determinations of a mixture of 0.5 µg mL-1 palladium and rhodium ions gave a mean absorbance of 0.058 and 0.053 with relative standard deviations of 1.8 and 1.6%, respectively. The developed method was successfully applied to the extraction and determination of the palladium and rhodium ions in road dust and standard samples and satisfactory results were obtained.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.