Application of artificial neural networks for the prediction of sulfur polycyclic aromatic compounds retention indices

Dual-Purpose SERS Sensor for Selective Determination of Polycyclic Aromatic Compounds via Electron Donor-Acceptor Traps

Toxic, carcinogenic, and mutagenic properties of polycyclic aromatic hydrocarbons (PAHs) and environmental pollution caused by polycyclic aromatic sulfur heterocycles (PASHs) postulate the importance of their selective and sensitive determination in environmental and oil fuel samples. Surface-enhanced Raman spectroscopy (SERS) opens up an avenue toward multiplex analysis of complex mixtures, however not every molecule gives high enhancement factors and, thus, cannot be reliably detected via SERS. However, the sensitivity can be drastically increased by additional resonant enhancement as a result of the analyte absorption band overlapping with the surface plasmon band of nanoparticles (NPs) and the laser excitation wavelength. Using this idea, we developed a dual-purpose SERS sensor based on trapping the target PAHs and PASHs into colored charge-transfer complexes (CTCs) with selected organic π-acceptor molecules on the surface of AgNPs. Studying, computing, and then comparing stability constants of the formed CTC served as a powerful explanation and prediction tool for a wise choice of π-acceptor indicator systems for the further silver surface modification. Moreover, we show that CTC formation can be effectively utilized for increasing both selectivity and sensitivity by simple liquid-liquid extraction prior to SERS measurements. For the first time, the dual-purpose SERS sensor allowed determination of two different classes of polycyclic aromatic fuel components down to 10 nM concentration, lower than that restricted by the ASTM regulation, and demonstrated multi-purpose capabilities of the developed approach.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Speciation of alkylated dibenzothiophenes through correlation of structure and gas chromatographic retention indexes

The gas chromatographic (GC) speciation of polycyclic aromatic sulfur heterocycles (PASH) in deeply desulfurized fuels is difficult without recourse to authentic standard compounds. Here we investigate the GC retention indexes for 30 alkylated dibenzothiophenes on two methylphenylsiloxane stationary phases of different selectivity (5% and 50% phenyl groups) with a view to avoid the synthesis of all congeners. The influence of the substitution pattern on the retention indexes is discussed. With the measured data it is possible to carry out a multiple linear regression (MLR) to calculate parameters for predicting the retention indexes of unknown polymethylated isomers based only on their structural features. Nine trimethyldibenzothiophene in a synthesis product are identified through their retention indexes. The retention indexes for a total of 43 alkyldibenzothiophenes are listed.