Fabrizio Ruggieri

Exposure to Di(2-ethylhexyl)phthalate in Humans during Pregnancy

Background: Di(2-ethylhexyl)phthalate (DEHP), the most commonly used plasticizer, is a widespread ubiquitous environmental contaminant. The potential health hazards from exposure to DEHP and its main metabolite, mono(2-ethylhexyl)phthalate (MEHP), have been well documented. Exposure to DEHP and MEHP in humans at risk, such as pregnant women and human fetuses, has not been tested. Methods: Plasma DEHP and MEHP concentrations were measured in a total of 24 consecutive mother-infant pairs by high performance liquid chromatography. Associations between DEHP/MEHP and infant characteristics were tested using Fisher’s exact test, unpaired t tests and univariate linear regression analysis. Results: Measurable DEHP and MEHP concentrations were found in 17/24 (70.8%) and 18/24 (75%) maternal plasmas, respectively, and in 11/25 (44%) and 18/25 (72.0%) cord samples, respectively. Either DEHP or MEHP were detectable in 21/24 (87.5%) maternal plasmas and 19/25 (76%) cord samples. The mean DEHP concentrations in maternal and cord plasmas were 1.15 ± 0.81 and 2.05 ± 1.47 µg/ml, respectively. The mean MEHP concentrations were 0.68 ± 0.85 and 0.68 ± 1.03 µg/ml, respectively. No significant correlations were found between maternal and cord blood DEHP, maternal and cord blood MEHP, maternal DEHP and cord blood MEHP, or maternal MEHP and cord blood DEHP plasma concentrations. Conclusion: Although the effects of perinatal exposure to phthalates need further research, our findings: (i) confirm the high frequency of DEHP and/or MEHP exposure in human pregnancies; (ii) indicate that the exposure to these environmental contaminants begins during intrauterine life, and (iii) suggest that fetal exposure is closely related to the maternal exposure.

Comparison of different sorbents for multiresidue solid-phase extraction of 16 pesticides from groundwater coupled with high-performance liquid chromatography

A procedure based on solid-phase extraction (SPE) followed by high-performance liquid chromatography (HPLC) with diode array detection has been developed for the simultaneous analysis of 16 widely used pesticides in groundwater samples. The compounds analysed were: aldicarb, atrazine, desethylatrazine, desysopropylatrazine, carbofuran, 2,4-D, dicloran, fenitrothion, iprodione, linuron, metalaxyl, metazachlor, phenmedipham, procymidone, simazine and vinclozolin. Five different SPE sorbents, C(18) bonded silica (Isolute SPE C18 (EC)), graphitised carbon black (Superclean Envi-Carb), highly cross-linked polystyrene-divinylbenzene (Lichrolut EN), divinylbenzene-N-vinylpyrrolidone (Oasis HLB) and surface modified styrene-divinylbenzene (Strata X), were compared. HPLC separation and quantification of the selected pesticides was carried out under isocratic conditions by means of a new reversed-phase column (Gemini from Phenomenex) based on C(18) bonded to organic-silica particles. Oasis HLB and Strata X provided the best results in the preconcentration of 1-l samples, yielding average recoveries higher than 70%, except for phenmedipham that rapidly degrades in groundwater. Detection limits of the target pesticides provided by the proposed SPE-HPLC procedure were between 0.003 and 0.04microg l(-1).

Impact of water quality on removal of carbamazepine in natural waters by N-doped TiO2 photo-catalytic thin film surfaces

Photocatalytic experiments on the pharmaceutical pollutant carbamazepine (CBZ) were conducted using sol-gel nitrogen-doped TiO(2)-coated glass slides under a solar simulator. CBZ was stable to photodegradation under direct solar irradiation. No CBZ sorption to the catalyst surface was observed, as further confirmed by surface characterization using X-ray photoelectron spectroscopic analysis of N-doped TiO(2) surfaces. When exposing the catalyst surface to natural organic matter (NOM), an excess amount of carbon was detected relative to controls, which is consistent with NOM remaining on the catalyst surface. The catalyst surface charge was negative at pH values from 4 to 10 and decreased with increasing pH, correlated with enhanced CBZ removal with increasing medium pH in the range of 5-9. A dissolved organic carbon concentration of 5mg/L resulted in ~20% reduction in CBZ removal, probably due to competitive inhibition of the photocatalytic degradation of CBZ. At alkalinity values corresponding to CaCO(3) addition at 100mg/L, an over 40% decrease in CBZ removal was observed. A 35% reduction in CBZ occurred in the presence of surface water compared to complete suppression of the photocatalytic process in wastewater effluent.

Near-field electrospinning of light-emitting conjugated polymer nanofibers

Ordered arrays of light-emitting conjugated polymer nanofibers are realized by near-field electrospinning.

Photocatalytic degradation of linuron in aqueous suspensions of TiO2

The photocatalytic degradation of linuron (3-(3,4-dichlorophenyl)-1-methoxy-1-methylurea), a widely used herbicide and potential environmental contaminant, in irradiated titanium dioxide aqueous suspensions is investigated. The photocatalytic performance of two nano-sized materials prepared by a sol–gel process, and the influence on the degradation rate of some operational parameters, such as catalyst amount, pH of the medium and hydrogen peroxide concentration, are evaluated. Within the range of the explored experimental conditions, degradation of linuron in an aqueous suspension of TiO2 at mg l−1 contamination levels is described by a pseudo-first-order kinetic model, the apparent half-life time of the herbicide being about 30 min in optimal conditions. Based on the evolution of total organic carbon and the fate of inorganic species, the disappearance of linuron seems to be accompanied by its almost quantitative mineralization.

Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling

Linear solvation energy relationships (LSERs) are commonly applied to model the effect of solute structure on the retention of analytes in reversed-phase high-performance liquid chromatography (RP-HPLC). Standard LSER approaches can be used, in principle, to predict RP-HPLC behaviour of unknown analytes under fixed separation condition. However, as solute structure is the only source of variability described by the model, a LSER established for a given column/eluent pair cannot be transferred to external separation conditions. In the present investigation, we attempt cross-column prediction by combining in the same model usual LSER molecular descriptors with observed retentions of selected solutes within the calibration set, adopted to represent the stationary phase features. A multi-layer artificial neural network (ANN) is used as regression tool to model the combined effect of solute structure and column on retention. This model is generated and validated using literature retention data of 34 solutes collected on 15 different RP-HPLC columns at a fixed eluent composition (acetonitrile-water 30:70, v/v). The calibration set is designed by selecting 25 solutes and 11 columns able to represent the variability of the chemical structure of the investigated compounds and dissimilarity of the stationary phases of the data set, respectively. The final predictive performance of the optimised ANN model is tested on the four columns excluded from calibration. Retention of the 25 solutes used to train the network and that of the nine unknown molecules on the external stationary phases is comparably well predicted.

Optimisation by response surface methodology of microextraction by packed sorbent of non steroidal anti-inflammatory drugs and ultra-high performance liquid chromatography analysis of dialyzed samples

A procedure based on microextraction by packed sorbent (MEPS) followed by ultra-high performance liquid chromatography (UHPLC) with photodiode array (PDA) detection has been developed for the analysis of seven selected non steroidal anti-inflammatory drugs (NSAIDs) in human dialysates. The influence on MEPS efficiency of pH of the sample, pH of the washing solvent and methanol content in the hydro-alcoholic elution mixture has been investigated by response surface methodology based on a Box-Behnken design of experiments. Among the above factors, pH of sample is the variable that mostly influences MEPS recovery. UHPLC separation of the NSAIDs was completed within less than 4min under isocratic elution conditions on a Fortis SpeedCore C18 column (150×4.6mm I.D., 2.6μm) using acetonitrile-phosphate buffer as the mobile phase. Calibration curves of the NSAIDs were linear over the concentration range 0.025-15μg/mL, with correlation coefficients≥0.998. Intra- and inter-assay relative standard deviations were <8% and recovery values ranged from 94% to 100% for the quality control samples. The results reveal that the developed MEPS/PDA-UHPLC method exhibits a good accuracy and precision and is well suited for the rapid analysis of human dialysate from patients treated with the selected NSAIDs.

Development of molecularly imprinted polymeric nanofibers by electrospinning and applications to pesticide adsorption

Novel polystyrene-based molecularly imprinted polymer nanofibers were synthesized through the electrospinning technique. The molecularly imprinted polymers were prepared using a non-covalent approach and atrazine as template. For comparison, nonimprinted polymer nanofibers were also synthesized. The morphology of the synthesized nanofibers was characterized using scanning electron microscopy. The adsorption of pesticides, atrazine, atrazine desisopropyl, atraton, carboxin, linuron, and chlorpyrifos was studied under equilibrium (batch) conditions. To describe the adsorption capability of the synthesized polymers, Langmuir and Freundlich models were used. The Freundlich model provided a better mathematical approximation of the sorption characteristic for polymers nanofibers. To evaluate the adsorption capacity in the presence of interferents experiments on river water samples spiked with a mixture of six pesticides were also performed. The results obtained for the highest concentration levels investigated, show a greater amount of pesticide adsorbed on molecularly imprinted polymers and non-imprinted polymers compared to those obtained using commercial stationary phases used as reference.

Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks

In this paper, we build a multiple-column retention model able to predict the behaviour of polychlorinated biphenyls (PCBs) in capillary gas-chromatography (GC) within a wide range of separation conditions. To this end, GC retention is related to both chemical structure of PCBs, encoded by selected theoretical molecular descriptors, and the kind of stationary phase, represented by the relative retention time (RRT) of a suitable small number of analytes. The model was generated using the retention data of 70 PCBs extracted from the pool of the 209 possible congeners collected on 17 different capillary columns featured by non-polar or moderately polar stationary phases, reported in the literature. Multilinear regression combined with genetic algorithm variable selection was preliminarily applied to generate a four-dimensional quantitative structure-retention relationship (QSRR) for each of the 17 columns, based on theoretical molecular descriptors extracted from the large set provided by the software Dragon. 33 molecular descriptors obtained by merging the non-common descriptors of various single-column QSRRs, combined with RRTs values of the less and the most retained PCB, were considered as the starting independent variables of the multiple-column retention model. A multi-layer artificial neural network (ANN), optimised on a validation set extracted from the calibration data, was applied to generate the multi-column retention model. The influence of starting inputs on the network output was evaluated by a sensitivity analysis and model complexity was reduced through a step-wise elimination of redundant molecular descriptors, while RRTs of further PCBs were included to improve description of the stationary phase. Nine molecular descriptors and RRTs of eight selected PCBs are considered as the independent variables of the final ANN-based model, whose predictive performance was tested on the 139 PCBs excluded from calibration and on six external columns and/or temperature programs.

Multi-variable retention modelling in reversed-phase high-performance liquid chromatography based on the solvation method: A comparison between curvilinear and artificial neural network regression

The linear solvation energy relationships (LSERs) have been widely used in the last decades for description and prediction of retention in reversed-phase high-performance liquid chromatography (RP-HPLC). LSERs are usually applied to model the effect of solute structure on the RP-HPLC retention at a fixed separation condition. Some authors by combining LSER with known empirical relationships relating retention with mobile phase composition of binary eluents (ϕ) have proposed a predictive model able to simultaneously relate RP-HPLC retention to both solute LSER descriptors and mobile phase composition. The resulting relationship can be established for a given column/organic modifier combination by curvilinear regression aimed at defining 18 model coefficients. In this study, we compare predictive performance of such approach and that of artificial neural network (ANN) regression in which the five solute LSER descriptors and ϕ are directly considered as the network inputs. To this purpose we analyse literature retention data of 31 molecules of different types collected on five reversed-phase columns either in water-acetonitrile and water-methanol mobile phase, the organic modifier content ranging between 20 and 70% (v/v). For each column/organic modifier combination both a curvilinear and an ANN-based model is built using data referred to 25 solutes, while the alternative models are later tested on the remaining six solutes excluded from calibration. Further, we compare capability of curvilinear and ANN regression after including into the respective models also variability related with the stationary phase, represented by the average retention of calibration solutes extrapolated at pure water as the mobile phase. The results of this investigation demonstrate that regardless of the kind of column and organic modifier ANN regression, as compared with curvilinear modelling, provides lower prediction errors and these are more uniformly distributed over the investigated retention range.