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In this work, pristine graphene oxide and its thermally reduced derivatives, rGO, were tested for the removal of triazines (atraton, prometryn, and atrazine) from water. The reduction process was optimized by means of design of experiments (DOE) coupled with response surface methodology (RSM), relying on the adsorption efficiency of the material. The optimal reduction conditions were calculated at a temperature of 110 °C maintained for 24 h; the mildest and simplest reduction protocol was chosen, as it allows in-air heat treatment with a common laboratory oven. The rGO samples were characterized before use, confirming a partial reduction process that, leaving intact most of the oxygenated functionalities on the graphene skeleton, may still allow favorable adsorption of pollutants through both hydrogen bonds and π-π interactions, which result from a large conjugated polyaromatic system. Triazine analyses were performed by high-performance liquid chromatography (HPLC); the data obtained from the adsorption isotherms were fitted with the Langmuir and Freundlich models, highlighting a slightly different adsorption behavior of atraton and prometryn compared with atrazine. Model outcomes were also used to support the hypotheses about the adsorption process.

Diclofenac (DCF) is one of the most widespread pharmaceutical compounds found in freshwaters as a pseudo-persistent pollutant due to its continuous release from point and diffuse sources, being its removal in Wastewater Treatment Plants incomplete. Moreover, DCF is particularly persistent in interstitial habitats and potentially toxic for the species that spend their whole life cycle among the same sediment grains. This study is aimed at offering a first contribution to the assessment of DCF effects on freshwater invertebrate species living in the interstitial habitats of springs, rivers, lakes and groundwaters. The Crustacea Copepoda are one of the main components of the freshwater interstitial communities, with the primacy taken by the worm-like and small-sized harpacticoids. A sub-lethal concentration of 50 μg L-1 DCF significantly affected six out of the eight behavior parameters of the burrower/interstitial crustacean harpacticoid Bryocamptus pygmaeus recorded by video tracking analysis. DCF exposure reduced swimming speed, swimming activity, exploration ability and thigmotaxis, and increased swimming path tortuosity. The biochemical approach revealed a reduced level of the mitochondrial superoxide dismutase 2 in individuals exposed to DCF. It could be explained by a decline in mitochondrial performance or by a reduced number of functional mitochondria. Since mitochondrial dysfunction may determine ATP reduction, it comes that less energy is produced for maintaining the cell functions of the DCF-exposed individuals. In addition, the increasing energy demand for the detoxification process further contributes to decrease the total energetic budget allocated for other physiological activities. These observations can explain the changes we have observed in the swimming behavior of the copepod B. pygmaeus.

An inductively coupled plasma-optical emission spectrometry (ICP OES) method was optimized and applied for determining the concentration of 14 elements (Ba, Ca, Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr, V, and Zn) in three representative white wines of the Abruzzo region (Italy). In order to optimise an ICP OES method a three level factorial design for three variables was used. The intensity of the emission lines for analytes was simultaneously maximised by using Derringer's desirability function. Using this approach, the optimal experimental conditions for wine analysis of 0.48 L min-1, 1.8 mL min-1 and 0.5 L min-1 for the nebulizer gas flow rate, sample uptake rate and auxiliary gas flow rate respectively were achieved. A total of 46 white wine samples of the three varieties were analysed by using the optimised experimental conditions. Linear Discriminant Analysis (LDA) and Partial Least Squares Linear Discriminant Analysis (PLS LDA) allowed an acceptable classification of the three varietal samples.

A Box–Behnken experimental design combined with response surface methodology was applied to investigate the effect of the eluent composition on the retention of common cations in ion chromatography. In particular, we modelled the retention factors of Na+, K+, NH4 +, Ca++ and Mg++ and the chromatogram resolution. Response surfaces were evaluated to assess interactive effects of nitric acid, pyridine-2,6-dicarboxylic acid and percentage of acetonitrile in the eluent on the above parameters. Nitric acid concentration and acetonitrile content appeared as the most and the least influential components of the eluent in determining the retention factors of individual ions or the global resolution of the chromatogram, while the concentration of the complexing agent exhibits an intermediate role. As expected, acidification of the mobile phase produces a decrease in the retention factors and a progressive worsening of resolution, and this effect can be modulated by the concentration of the complexing agent. The proposed approach seems suitable to investigate the retention phenomena in ion chromatography under application of complex eluent phases.

One hundred and forty-nine (149) Italian saffron samples produced in the years from 2013 to 2016 in distinct sites located in five different Italian regions, Abruzzo, Tuscany, Umbria, Campania and Sardinia, together with twenty-seven (27) commercial samples, have been analyzed by high-performance liquid chromatography with diode array detection (HPLC-DAD). Among the investigated samples, those produced in Abruzzo (L'Aquila area) present an even higher added-value, because, since 2005, saffron of L'Aquila has been granted of the protected designation of origin (PDO) mark. In the present study, two different analytical approaches aimed at distinguishing PDO saffron of L'Aquila from the other samples have been compared. The first strategy is a more traditional approach, where the chromatograms collected at specific wavelengths are classified by Partial Least Squares Discriminant Analysis (PLS-DA). The second strategy exploits the multi-way nature of data, avoiding discarding any source of information. Consequently, the entire spectro-chromatogram is handled by N-Partial Least Squares (N-PLS) and then classified by Linear Discriminant Analysis (LDA). Both approaches provided satisfactory predictions; the best results from the prediction point of view (estimated on an external set of samples) were achieved by the proposed multi-way methodology.

Mountain karst ponds are sensitive environments, hosting complex trophic networks where amphibians play a major role, often as top predators. The diet of the Italian crested newt (Triturus carnifex) is still poorly known for populations occupying mountain karst ponds. These are traditionally used as livestock&rsquo, s watering points, leading to water pollution due to excreta and wading behavior. The aim of this paper is to understand the relationship between T. carnifex diet composition, assessed through the stomach flushing technique, and physical and chemical characteristics in mountain ponds, focusing on parameters altered by livestock pressure, such as ammonium concentration and dissolved oxygen. The high diversity of prey items found within the newts&rsquo, gut contents confirms the generalist diet even in mountain ponds. The number of prey taxa, their relative abundance and Shannon&ndash, Wiener diversity index show variations among the sampled sites, related to livestock organic pollution. Moreover, we report the very first European records of microplastic items in amphibians&rsquo, stomach content, which also represent the first evidence for Caudata worldwide. Our findings suggest that livestock pressure directly influences T. carnifex diet and highlight that the emerging issue of plastics is a threat even in remote high-altitude environments.

The simultaneous influence of pH and composition of water–ethanol mixtures on the extraction from tea of caffeine (CF), gallic acid (GA) and the selected catechins epicatechin (EC), epicatechin-3-gallate (ECG) and epigallocatechin-3-gallate (EGCG) is investigated by response surface methodology. Extraction experiments are carried out at room temperature according to a three-level full-factorial design in which pH, measured before mixing with ethanol, is varied between 6 and 8 and volume fraction of ethanol is varied between 30 and 70 % v/v. Response surfaces are determined by fitting of extracted amounts of the above substances, determined by HPLC analysis, with a second-degree polynomial model. Within the investigated experimental domain, extraction efficiency of CF is substantially the same and extraction of ECG and EGCG is not affected by acidity of the medium while both pH and composition influence the extraction of EC and GA.

PDO (protected designation of origin) Zafferano dell’Aquila (AQ), Iranian (IR), and commercial (CS) saffron samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The ICP-MS data of 30 elements were handled by unsupervised (principal component analysis (PCA)) and supervised (linear discriminant analysis (LDA)) multivariate statistical methods to identify a subset of discriminant variables useful for geographical classification. Moreover, class modeling of AQ saffron was performed by both UNEQ (unequal disperses classes) and SIMCA (soft independent modeling of class analogy) methods. A good differentiation of the AQ, IR, and CS samples was obtained by the LDA based on four selected elements: Sr, Ca, Mo, and Fe. A UNEQ class model for the PDO AQ saffron based on the above four elements provided 100% sensitivity (all authentic AQ saffron were accepted) and 100% specificity (all IR and CS were rejected), while slightly worse results were obtained using the SIMCA (89% sensitivity and 96% specificity).

Sixty-five samples of red garlic (Allium sativum L.) coming from four different production territories of Italy were analysed by means of inductively coupled plasma optical emission spectrometry. The garlic samples were discriminated according to the geographical origin using the content of seven elements (Ba, Ca, Fe, Mg, Mn, Na and Sr). Both classification and class modelling methods by using linear discriminant analysis (LDA) and soft independent model class analogy (SIMCA), respectively, were applied. Classification ability and modelling efficiency were evaluated on an external prediction set (21 garlic samples) designed by application of duplex Kennard-Stone algorithm. All the calibration and prediction samples were correctly classified by means of LDA. The class models developed using SIMCA exhibited high sensitivity (almost all the calibration and external samples were accepted by the respective classes) and good specificity (the majority of extraneous samples were refused by each class model).

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.

Response surface methodology (RSM) was applied to optimise the extraction of curcuminoids (curcumin, demethoxycurcumin and bisdemethoxycurcumin) from turmeric using ethyl lactate (EL), ethanol and water under mild conditions (magnetic stirring at room temperature). An augmented simplex-centroid mixture design was used to monitor the dependence of the extraction efficiency from the proportions of the three solvents in the extraction medium. HPLC was used to establish the content of curcuminoids in turmeric and in the extracts. Surface plots for the extracted amount of each curcuminoid covering the whole composition domain were generated by interpolation of the experimental data with quadratic canonical polynomial models. The response surfaces of the three curcuminoids are qualitatively similar and the maximum extraction efficiency was obtained with water-EL 30:70v/v that ensured the almost quantitative recovery of the three compounds from turmeric. While degradation of the three curcuminoids in water at moderate alkaline pH is relatively fast (half-times are between 0.23 and 8.5h at pH=8.6), their stability is noticeably greater in EL (half-times are within 21-69days). Addition of EL to water is also able to inhibit the alkaline hydrolysis of curcumin and its derivatives, their half-times in the water-EL 30:70v/v, being within 40-70h at pH=8.6. The above evidences suggest that EL is a promising solvent for the extraction of curcuminods from turmeric and a suitable medium for vehiculation of these compounds into drugs or foods.

In the present work, the retention time (RT) of acylcarnitines, collected by ultra-performance liquid-chromatography after formation of butyl esters, is modelled by quantitative structure–retention relationship (QSRR) method. The investigated set consists of free carnitine and 46 different acylcarnitines, including the isomers commonly monitored in screening metabolic disorders. To describe the structure of (butylated) acylcarnitines, a large number of computational molecular descriptors generated by software Dragon are subjected to variable selection methods aimed at identifying a small informative subset. The QSRR model is established using two different approaches: the multi linear regression (MLR) combined with a genetic algorithm (GA) variable selection and the partial least square (PLS) regression after iterative stepwise elimination (ISE) of useless descriptors. Predictive performance of both models is evaluated using an external set consisting of 10 representative acylcarnitines, and, successively, by repeated random data partitions between the calibration and prediction sets. Finally, a principal component analysis (PCA) is performed on the model variables to facilitate the interpretation of the established QSRRs. A PLS model based on seven latent variables extracted from 20 molecular descriptors selected by ISE permits to calculate/predict the retention time of acylcarnitine with accuracy better than 5%, whereas a 6-dimensional model identified by GA-MLR provides a slightly worse performance.

In this paper, a multilayer artificial neural network is used to model simultaneously the effect of solute structure and eluent concentration profile on the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient elution. The retention data of 24 triazines, including common herbicides and their metabolites, are collected under 13 different elution modes, covering the following experimental domain: starting acetonitrile volume fraction ranging between 40 and 60% and gradient slope ranging between 0 and 1% acetonitrile/min. The gradient parameters together with five selected molecular descriptors, identified by quantitative structure-retention relationship modelling applied to individual separation conditions, are the network inputs. Predictive performance of this model is evaluated on six external triazines and four unseen separation conditions. For comparison, retention of triazines is modelled by both quantitative structure–retention relationships and response surface methodology, which describe separately the effect of molecular structure and gradient parameters on the retention. Although applied to a wider variable domain, the network provides a performance comparable to that of the above “local” models and retention times of triazines are modelled with accuracy generally better than 7%.

Photocatalytic experiments on the pharmaceutical pollutant carbamazepine (CBZ) were conducted using sol-gel nitrogen-doped TiO-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 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 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. © 2012 Elsevier B.V.

Nitric oxide plays a pivotal role in regulating vascular tone. Different studies show endothelial function is impaired during hyperglycemia. Dark chocolate increases flow-mediated dilation in healthy and hypertensive subjects with and without glucose intolerance; however, the effect of pretreatment with dark chocolate on endothelial function and other vascular responses to hyperglycemia has not been examined. Therefore, we aimed to investigate the effects of flavanol-rich dark chocolate administration on (1) flow-mediated dilation and wave reflections; (2) blood pressure, endothelin-1 and oxidative stress, before and after oral glucose tolerance test (OGTT). Twelve healthy volunteers (5 males, 28.2±2.7 years) randomly received either 100 g/d dark chocolate or flavanol-free white chocolate for 3 days. After 7 days washout period, volunteers were switched to the other treatment. Flow-mediated dilation, stiffness index, reflection index, peak-to–peak time, blood pressure, endothelin-1 and 8-iso-PGF 2α were evaluated after each treatment phase and OGTT. Compared with white chocolate, dark chocolate ingestion improved flow-mediated dilation ( P =0.03), wave reflections, endothelin-1 and 8-iso-PGF 2α ( P P =0.027), 6.74±0.51 ( P =0.046) at 1 and 2 h after the glucose load, respectively. Similarly, after white chocolate but not after dark chocolate, wave reflections, blood pressure, and endothelin-1 and 8-iso-PGF 2α increased after OGTT. OGTT causes acute, transient impairment of endothelial function and oxidative stress, which is attenuated by flavanol-rich dark chocolate. These results suggest cocoa flavanols may contribute to vascular health by reducing the postprandial impairment of arterial function associated with the pathogenesis of atherosclerosis.

N-doped TiO2 nanofibers were deposited by electrospinning onto silicon and then annealed in air. They have been analyzed by secondary electron microscopy, X-ray diffraction, and X-ray photoemission. After the thermal processes, the fibers, having a diameter between 300 and 400 nm, contain a small amount of nitrogen and still show the TiO2 anatase crystalline structure. Different solutions and annealing processes were used to vary the nitrogen concentration and the crystalline phase. These nanofibers were successfully tested as photocatalytic devices for methylene blue degradation under visible light irradiation.

In this paper, we use a quantitative structure–retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column (1 t R and 2 t R, respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for 1 t R and 2 t R from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling 1 t R and 2 t R, respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model 1 t R and 2 t R without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for 1 t R and 2 t R in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components.

Recently, we have proposed an approach to multi-column RP-HPLC retention modelling under isocratic conditions based on a combination of five molecular descriptors, the volume fraction of organic modifier in the mobile phase and a column descriptor, simultaneously considered as dependent variables of artificial neural network (ANN) regression. The column descriptor, in particular, was identified with the observed average retention of the solutes used in calibration extrapolated to pure water as the mobile phase. The ANN-based model was seen to accurately describe retention on a pool of octade-cylsiloxane-bonded (C 18 ) columns in a wide range of mobile phase composition. Reliability of this approach is further examined here by analysing the retention data of Reta et al. (Anal. Chem. 1999, 71, 3484―3496) referring to 17 aromatic compounds collected in water-methanol mobile phases at the compositions 45, 50, 55 and 60% v/v of methanol with eight different columns based on various hydrocarbon, fluorocarbon and aromatic bonded stationary phases. Further, in this study we compare the explanatory capability of two different kinds of molecular descriptors: the well-known solvatochromic descriptors and theoretical descriptors extracted by genetic algorithm variable selection from the large set provided by the popular software Dragon.

This paper analyses flow and transport of pesticides from the unsaturated zone to groundwater so as to predict concentration of those contaminants in the Fucino Plain’s groundwater, by site investigations and numerical simulations. Pesticides were detected in surface water (peaks of 13 μg L−1) and groundwater (peaks of 0.37 μg L−1). Modelling tools made it possible to identify that pattern of precipitation, organic matter content, and root thickness are the key factors involved in vertical seepage of pesticides. Numerical simulations indicated that a significant fraction of contaminants is leached from the most surficial soil layers through runoff, while only a secondary fraction is mobilised towards groundwater. Likelihood of contaminating deep groundwater is fairly low, whereas surface waters show higher susceptibility. Results of the proposed conceptual hydrogeological model show that pesticides are more likely to be entrained by mixing of stream water with shallow groundwater in periods of high water exploitation from shallow wells.

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the retention of pesticides in reversed-phase high-performance liquid chromatography (RP-HPLC) is investigated. To this end, two retention data sets previously collected in water–acetonitrile mobile phase are re-examined. The first data set ( data-set-1 ) consists of retention data of 26 neutral compounds belonging to widely used pesticide classes, collected within the mobile phase composition range 40–65% (v/v) acetonitrile. The second data set ( data-set-2 ) describes retention of phenoxy acid herbicides and structurally related compounds (benzoic acid and phenylacetic acid derivatives), as a whole covering the p K a range 2.3–4.3, as a function of mobile phase composition, ranging between 30 and 70% (v/v) acetonitrile, and pH, ranging between 2 and 5. For each data set, the mobile phase attributes are combined with WHIM or GETAWAY descriptors into “mixed” predictive models in order to attempt retention modelling within the whole mobile phase composition range of analytical interest. Six- or seven-dimensional multilinear models, preliminarily selected using a genetic algorithm, were improved using a multi-layer artificial neural network (ANN) learned by back propagation. ANN performance was tested on three molecules not used in the learning stage and by leave-more-out cross validation. The results reveal that while WHIM descriptors seem not adequate to model retention of solutes of data-set-1 , GETAWAY descriptors provide a satisfactory retention model. On the other hand WHIM and GETAWAY descriptors applied to data-set-2 provide a similar performance, even if slightly worse as compared with the above case. Accuracy of retention modelling in these cases is comparable or slightly poorer as compared with the results previously obtained by combining quantum chemical descriptors or usual physico-chemical solute properties (log k ow and p K a ) and mobile phase attributes.

One hundred and forty-four Italian saffron samples produced in the years from 2009 to 2015 in five distinct areas located in four different regions, Abruzzo (L'Aquila), Tuscany (Florence), Umbria (Cascia and Citta della Pieve) and Sardinia, have been analysed by high-performance liquid chromatography with diode array detection. Intensities of the chromatographic peaks attributed to crocins, safranal, picrocrocin and its derivatives and flavonoids were considered as variables in linear discriminant analysis to attempt geographical classification. The results revealed that spices produced at different sites of the Italian territory can be discriminated with good accuracy. The differentiation of saffron cultivated in Sardinia from those produced in Central Italy was mainly attributed to different contents of the most abundant crocins. Good differentiation of spices produced in close sites of Central Italy was also observed, 88% of validation samples being correctly classified; some minor crocins are responsible for such discrimination.

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 were8% 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.

A sensitive high performance liquid chromatographic (HPLC) method with ultraviolet absorption detection (230 nm) was developed and validated for the determination of a phosphodiesterase V inhibitor, Sildenafil, in seminal plasma. A single step liquid–liquid extraction procedure using ethyl acetate was performed to recover sildenafil from 1.0 mL of seminal plasma combined with 200 µL of NaOH 0.1 M. A symmetry C18 column (150 × 4.6 mm I.D. 5 µm) was used as a stationary phase and the mobile phase consisted of 32% acetonitrile and 68% phosphoric acid (0.016 M; pH 5.3) at a flow rate of 1.0 mL/min. The quantitation limit was 5 ng/mL. Intra‐ and inter‐day relative standard deviation (RSD) did not exceed 6.6%. This HPLC method has been successfully used in medical laboratories to assay seminal plasma samples for studies on the treatment with sildenafil.

A method for the simultaneous determination of di(2-ethylhexyl)phthalate and its major metabolite mono(2-ethylhexyl)phthalate in human plasma by high-performance liquid chromatography is described. The procedure, linear in the concentration range 0.05–5.0 ppm, is simple and rapid and allows accurate and precise results. The limits of detection for di(2-ethylhexyl)phthalate and mono(2-ethylhexyl)phthalate were 0.05 ppm.

A response surface methodology (RSM) approach is applied to optimise the temperature-programme gas-chromatographic separation of 16 organochloride pesticides, including 12 compounds identified as highly toxic chemicals by the Stockholm Convention on Persistent Organic Pollutants. A three-parameter relationship describing both linear and curve temperature programmes is derived adapting a model previously used in literature to describe concentration gradients in liquid chromatography with binary eluents. To investigate the influence of the three temperature profile descriptors (the starting temperature, the gradient duration and a shape parameter), a three-level full-factorial design of experiments is used to identify suitable combinations of the above variables spanning over a useful domain. Resolutions of adjacent peaks are the responses modelled by RSM using two alternative methods: a multi-layer artificial network (ANN) and usual polynomial regression. The proposed ANN-based approach permits to model simultaneously the resolutions of all the consecutive analyte pairs as a function of the temperature profile descriptors. Four critical pairs giving partially overlapped peaks are identified and multiresponse optimisation is carried out by analysing the surface plot of a global resolution defined as the average of the resolutions of the critical pairs. Descriptive/predictive performance and applicability of the ANN and polynomial RSM methods are compared and discussed.

Development of chromatographic analyses of synthetic cannabinoids is complicated by the lack of commercial reference standards, especially for new analogues introduced in the clandestine market to bypass legal controls and for their metabolites. In the present work, we explore the possibility of predicting the retention behaviour of the cannabimimetic aminoalkilindoles and their urinary metabolites in high-performance liquid-chromatography using a quantitative structure-retention relationship (QSRR) generated by multilinear regression. To represent the structure of the 43 investigated analytes, 617 computational molecular descriptors are subjected to genetic algorithm variable selection aimed at identifying a small but informative subset. Predictive performance of the QSRR model is evaluated on an external set consisting of 10 representative compounds, including both drugs and their metabolites, and, successively by a Monte Carlo validation method. The best QSRR model, based on six molecular descriptors, exhibits a promising predictive performance and robustness.

Light-emitting nanostructures made by conjugated polymers show interesting emission and electronic properties. In this work we report on novel approaches for the fabrication and control of light-emitting nanofibers by electrospinning. The shape, size and light-emitting properties of the fibers can be specifically tailored by acting on the composition of the solution used for the electrospinning process, an approach allowing for obtaining fibers ranging from micrometer-sized ribbons to almost cylindrical fibers with diameters down to few hundreds of nanometers. Moreover, following proper process optimization these fibers can also be precisely positioned in ordered arrays by near-field electrospinning, a method that exploits the stable region of the polymer jet. The possibility of precisely shaping the conjugated polymer fibers and of assembling the fiber in ordered arrays, combined with enhanced emission properties, opens interesting perspectives for developing novel emitting flexible nanomaterials suitable for light sourcing and optical sensing.

A multilayer artificial neural network (ANN) is used to model the reversed-phase liquid chromatography retention times of 16 selected compounds, including purines, pyrimidines and nucleosides. The analysed data, taken from literature, were collected in acetonitrile-water eluents under the application of 16 different multilinear gradients. The parameters describing the gradient profile together with solute descriptors are considered as the independent variables of an ANN-based model providing the retention time as response. Categorical variables or, alternatively, a selected set of molecular descriptors of computational origin are adopted to represent the solutes. Network training, validation and testing are performed preliminarily using data of 12, 2 and 4 gradients, respectively and successively, to investigate model performance under more severe calibration conditions, with data of 9, 2 and 7 gradients. The proposed approach allows a quite accurate prediction of retention times of the target analytes in external multilinear gradients. Categorical variables can successfully represent the target solutes when the model is called to transfer retention data from calibration to external gradients. In particular, using a five-dimensional bit string to represent the analytes, mean errors on retention times are 2 and 3 % under the most and less favourable calibration conditions, respectively. A comparable performance is observed if the categorical variables are replaced by five molecular descriptors, selected by a genetic algorithm within a large set of structural variables of computational origin.

In this paper, a multilayer artificial neural network is used to model simultaneously the effect of solute structure and eluent concentration profile on the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient elution. The retention data of 24 triazines, including common herbicides and their metabolites, are collected under 13 different elution modes, covering the following experimental domain: starting acetonitrile volume fraction ranging between 40 and 60% and gradient slope ranging between 0 and 1% acetonitrile/min. The gradient parameters together with five selected molecular descriptors, identified by quantitative structure-retention relationship modelling applied to individual separation conditions, are the network inputs. Predictive performance of this model is evaluated on six external triazines and four unseen separation conditions. For comparison, retention of triazines is modelled by both quantitative structure-retention relationships and response surface methodology, which describe separately the effect of molecular structure and gradient parameters on the retention. Although applied to a wider variable domain, the network provides a performance comparable to that of the above "local" models and retention times of triazines are modelled with accuracy generally better than 7%.

We combine computational molecular descriptors and variables related with the gas-chromatographic stationary phase into a comprehensive model able to predict the retention of solutes in external columns. To explore the quality of various approaches based on alternative column descriptors, we analyse the Kovats retention indices (RIs) of 90 saturated esters collected with seven columns of different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP). Cross-column retention prediction is evaluated on an internal validation set consisting of data of 40 selected esters collected with each of the seven columns, sequentially excluded from calibration. The molecular descriptors are identified by a genetic algorithm variable selection method applied to a large set of non-empirical structural quantities aimed at finding the best multi-linear quantitative structure–retention relationship (QSRR) for the column OV-25 having intermediate polarity. To describe the columns, we consider the sum of the first five McReynolds phase constants and, alternatively, the coefficients of the corresponding QSRRs. Moreover, the mean RI value for the subset of esters used in QSRR calibration or RIs of a few selected compounds are used as column descriptors. For each combination of solute and column descriptors, the retention model is generated both by multi-linear regression and artificial neural network regression.

Ordered arrays of light-emitting conjugated polymer nanofibers are realized by near-field electrospinning., The authors report on the realization of ordered arrays of light-emitting conjugated polymer nanofibers by near-field electrospinning. The fibers, made from poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene], have diameters of a few hundreds of nanometers and their emission peaked at 560 nm. The observed blue-shift compared to the emission from reference films is attributed to different polymer packing in the nanostructures. Optical confinement in the fibers is also analyzed through self-waveguided emission. These results open interesting perspectives for the realization of complex and ordered architectures by light-emitting nanofibers, such as photonic circuits, and for the precise positioning and integration of conjugated polymer fibers into light-emitting devices.

Titanium dioxide (TiO2), thanks to its interesting properties as non-toxicity, low cost and high chemical stability, has been extensively investigated for several applications in which, following light absorption, the generated charges can be usefully applied, as for photovoltaic systems and photocatalytic devices. However, due to the wide intrinsic energy gap of TiO2 (between 3.0 and 3.2 eV, depending on the crystalline structure), only a small fraction of the solar spectrum can be used to promote the electron–hole pair formation. In order to reduce the energy gap and, therefore, increase the fraction of the solar spectrum that can be absorbed, different approaches have been used, mainly by doping TiO2 with metals or anions. In this work we will present our results on the deposition, by means of the electrospinning technique, of TiO2 nanofibres doped with metals (iron, copper and tungsten). These nanofibres exhibit morphology at bundle structure composed by sheaths of about 300-nm diameter and core filled with 20-nm-thick fibrils. Metal-doped TiO2 nanofibres have been characterized by SEM, TEM, EDX, XPS and XRD for morphological, chemical and structural analyses, respectively. The effect of the metal concentration on the photocatalytic properties of these nanofibres have been investigated through the study of the degradation of a pollutant model (rhodamine 6G) by using visible lamp and sunlight as light source.

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.

Some predictive approaches aimed at modelling the combined effect of solute molecular structure and mobile phase composition on retention in reversed-phase high-performance chromatography (RP-HPLC) have been developed in the literature. These models are established for a given binary eluent (normally acetonitrile–water or methanol–water) by non-linear (curvilinear or artificial neural network) regression assuming as the mobile phase descriptor the volume fraction φ of the organic modifier. In the present investigation, we propose a model applicable simultaneously to acetonitrile–water and methanol–water eluents. To this end, the Kamlet-Taft solvatochromic descriptors of the eluent and the solvatochromic descriptors of the analytes are considered as the input variables of a multi-layer artificial neural network (ANN) providing the solute retention as the response. This approach is applied to a set of 31 molecules analyzed with five different columns in the φ range 20–70 % at 10 % steps for both acetonitrile- and methanol-containing mobile phases. For each column, an ANN-based model is built using retention data of 25 molecules selected by the Kennard-Stones algorithm while retention data of the unselected six solutes are considered in the final evaluation of predictive performance of the trained network. To test cross-eluent prediction, the network optimized for a given column was successively trained with data collected in eight out of 12 eluents and applied to deduce retention in the four remaining mobile phases. The results reveal that RP-HPLC behavior of external solutes is quite accurately modelled in the whole explored composition range of acetonitrile– and methanol–water mobile phases. Moreover, the model exhibits a promising capability of deducing retention of external solutes even in unknown eluents.

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.

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 [20]. 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.

Aromatic imines react with phenylacetylene or styrene in an acetonitrile solution of iron(III) chloride to give quinolines 3 or their tetrahydro derivatives 11 together with variable amounts of products 4 arising from the reduction of the imines. The initial atep appears to be a one-electron oxidation to generate iron(II) and an imine radical cation. When the reactions are carried out in the presence of stoichiometric amounts of tetrachloro-p-benzoquinone (chloranil), only quinolines 3 are obtained

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.

Recently, we have proposed an approach to multi-column RP-HPLC retention modelling under isocratic conditions based on a combination of five molecular descriptors, the volume fraction of organic modifier in the mobile phase and a column descriptor, simultaneously considered as dependent variables of artificial neural network (ANN) regression. The column descriptor, in particular, was identified with the observed average retention of the solutes used in calibration extrapolated to pure water as the mobile phase. The ANN-based model was seen to accurately describe retention on a pool of octadecylsiloxane-bonded (C(18)) columns in a wide range of mobile phase composition. Reliability of this approach is further examined here by analysing the retention data of Reta et al. (Anal. Chem. 1999, 71, 3484-3496) referring to 17 aromatic compounds collected in water-methanol mobile phases at the compositions 45, 50, 55 and 60% v/v of methanol with eight different columns based on various hydrocarbon, fluorocarbon and aromatic bonded stationary phases. Further, in this study we compare the explanatory capability of two different kinds of molecular descriptors: the well-known solvatochromic descriptors and theoretical descriptors extracted by genetic algorithm variable selection from the large set provided by the popular software Dragon.

Aromatic imines react with phenylacetylene or styrene in an acetonitrile solution of iron(III) chloride to give quinolines 3 or their tetrahydro derivatives 11 together with variable amounts of products 4 arising from the reduction of the imines. The initial atep appears to be a one-electron oxidation to generate iron(II) and an imine radical cation. When the reactions are carried out in the presence of stoichiometric amounts of tetrachloro-p-benzoquinone (chloranil), only quinolines 3 are obtained