Ionic hydrophobic deep eutectic solvents in developing air-assisted liquid-phase microextraction based on experimental design: Application to flame atomic absorption spectrometry determination of cobalt in liquid and solid samples

This paper reports a new and simple microextraction procedure for cobalt determination using green ionic hydrophobic deep eutectic solvent in developing air-assisted liquid-phase microextraction and flame atomic absorption spectrometry. Thecomplexationof Co(II) ions was carried out by using dithizone solution as complexing agent at pH5.The key variables affecting microextraction steps were optimized by response surface methodology (RSM) based on central composite design. Under the optimum microextraction conditions, calibration graph was linear in the range of 0.1-500 µg L^-1 Co(II) with correlation coefficient of 0.9985. Additionally, detection limit, quantitation limit and enrichment factor were found to be 0.04 µg L^-1, 0.1 µg L^-1 and 175, respectively. The reproducibility and repeatability were ≤ 2.9% and ≤ 3.6%, respectively. Based on the results obtained, the proposed methodology has been successfully employed for Co analysis in liquid and solid samples with recovery range of 94.2-105%.

Quantification of tributyltin in seawater using triple isotope dilution gas chromatography-inductively coupled plasma mass spectrometry achieving high accuracy and complying with European Water Framework Directive limits

A triple isotope dilution GC-ICPMS method for the determination of tributyltin (TBT) was developed and validated to meet the European Water Framework Directive (WFD) requirements. The validation procedure involved the evaluation of trueness, precision (repeatability, intermediate precision), limit of detection (LOD) and limit of quantification (LOQ), stability, measurement uncertainty and traceability studies. The method is one of the most sensitive methods published to date with good accuracy, 103% average recovery in the range with %RSDs of 2.8-6.7%. A LOD value of 0.015 ng L^-1 for the TBT cation was achieved with a sample volume of 12 mL seawater. TBT was derivatized using 20 µL sodium tetraethylborate solution (0.05% NaBEt₄) to make volatile for GC-ICPMS. Measurement uncertainty was in the range of 4.8-13% which was achieved through dissolution of tributyltinchloride (TBTCl) in 1-propanol, a low-volatility solvent combined with the use of a triple isotope dilution (ID) calibration technique. Isotope dilution calibration was performed by adding ¹¹⁷Sn isotopically enriched TBT to the seawater samples. The stability test results showed that TBT concentration was stable for three months in seawater samples after passing through a 0.2 µm filter and stored in amber glass bottles at 4°C. The response surface methodology (RSM) approach was successfully implemented to provide optimal conditions for large volume injection (LVI) to obtain the maximum analytical signal. The key variables selected in the experimental design were evaporation time, evaporation temperature, carrier flow, and injection speed. This method was applied to seawater samples collected from the Bay of Izmit, Kocaeli, Turkey, where TBT pollution has not been measured yet.

Simple and Green Heat-Induced Deep Eutectic Solvent Microextraction for Determination of Lead and Cadmium in Vegetable Samples by Flame Atomic Absorption Spectrometry: a Multivariate Study

In this study, a simple, green, and cheap analytical procedure based on heat-induced deep eutectic solvent microextraction (HI-DES-ME) coupled with flame atomic absorption spectrometer (FAAS) was developed for the determination of Pb(II) and Cd(II) in vegetables. After the preliminary experiment, response surface methodology (RMS) based on central composite design (CCD) was used for the optimization of critical factors such as pH of sample solution, amount of extraction solvent, temperature, and amount of ligand. Microwave step was applied for the digestion of vegetable samples. Under optimum conditions obtained by the CCD, calibration graphs for Pb(II) and Cd(II) were linear in the concentration range of 0.5-250 and 1.0-300 ng mL^-1, respectively. Limits of detection (LODs) and sensitivity enhancement factor (SIFs) were found in the range of 0.17-0.35 ng mL^-1 and 93-67 ng mL^-1, respectively. Relative standard deviations (N = 10, RSDs%) for Pb(II) (10 ng mL^-1) and Cd(II) (50 ng mL^-1) were 3.7% and 2.3%, respectively. In order to validate the proposed method, certified reference material (CRM) and spiked samples were used. Experimental results showed that there was no significant difference between the obtained and certified values. Then, the proposed method was successfully applied for the preconcentration and determination of Pb(II) and Cd(II) in different vegetables.

Vortex assisted-ionic liquid dispersive liquid-liquid microextraction and spectrophotometric determination of quercetin in tea, honey, fruit juice and wine samples after optimization based on response surface methodology

The aim of our study is to develop a new vortex assisted-ionic liquid dispersive liquid-liquid microextraction (VA-IL-DLLM) method for preconcentration and determination of the quercetin in tea, honey, fruit juice and wine samples by spectrophotometry. The method is based on pH sensitive ion-pair formation between quercetin and rhodamine B at pH 6.5 by donor-acceptor mechanism, and then dispersion of the complex in the fine-drops of ionic liquid (IL). In this context, the effects of pH, concentrations of ion-pairing reagent, the IL, vortex time and dispersive solvent type on the preconcentration process of quercetin were investigated using a 2-level-5-factor central composite half fraction design (CCD) as experimental design for response surface methodology (RSM). Quantitative model was developed to determine the quercetin in food samples, and it is verified by analysis of variance (ANOVA) at a 95% confidence level (P < 0.05). Response surface plots and contour plots obtained by the model are used to determine the interactions of experimental variables. After the optimization, calibration graph was obtained between 35 and 750 μg L^-1 with the detection limit of 10.2 μg L^-1. The recovery and relative standard deviations (RSD%) were in range of 94-104%, and in range of 2.5-4.2%, respectively. The accuracy and precision of the method were tested by the experimental studies such as recoveries, intermediate precision, trueness and expanded uncertainty. A comparison of the current results to those reported for other studies is also presented.

Geochemical and spectroscopic investigations of Cd and Pb sorption mechanisms on contrasting biochars: engineering implications

Biochars prepared from nut shells, plum stones, wheat straws, grape stalks and grape husks were tested as potential sorbents for Cd and Pb. Mechanisms responsible for metal retention were investigated and optimal sorption conditions were evaluated using the RSM approach. Results indicated that all tested biochars can effectively remove Cd and Pb from aqueous solution (efficiency varied between 43.8% and 100%). The removal rate of both metals is the least affected by the biochar morphology and specific surface but this removal efficiency is strongly pH-dependent. Results of variable metal removal combined with different optimized conditions explain the different metal sorption mechanisms, where the predominant mechanism is ion exchange. In addition, this mechanism showed very strong binding of sorbed metals as confirmed by the post-desorption of the fully metal-loaded biochars. Finally, these biochars could thus also be applicable for metal contaminated soils to reduce mobility and bioavailability of Cd and Pb.

Trace elements and nutrients adsorption onto nano-maghemite in a contaminated-soil solution: A geochemical/statistical approach

Two experiments were carried out to study the competition for adsorption between trace elements (TEs) and nutrients following the application of nano-maghemite (NM) (iron nano-oxide; Fe2O3) to a soil solution (the 0.01molL(-1) CaCl2 extract of a TEs-contaminated soil). In the first, the nutrients K, N, and P were added to create a set of combinations: potential availability of TEs during their interaction with NM and nutrients were studied. In the second, response surface methodology was used to develop predictive models by central composite design (CCD) for competition between TEs and the nutrients K and N for adsorption onto NM. The addition of NM to the soil solution reduced specifically the concentrations of available As and Cd, but the TE-adsorption capacity of NM decreased as the P concentration increased. The CCD provided more concise and valuable information, appropriate to estimate the behavior of NM sequestering TEs: according to the suggested models, K(+) and NH4(+) were important factors for Ca, Fe, Mg, Mn, Na, and Zn adsorption (Radj(2)=95%, except for Zn with Radj(2)=87%). The obtained information and models can be used to predict the effectiveness of NM for the stabilization of TEs, crucial during the phytoremediation of contaminated soils.

Chemometric evaluation of the heavy metals distribution in waters from the Dilovasi region in Kocaeli, Turkey

The main objective of this study was to test water samples collected from 10 locations in the Dilovası area (a town in the Kocaeli region of Turkey) for heavy metal contamination and to classify the heavy metal (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) contents in water samples using chemometric methods. The heavy metals in the water samples were identified using inductively coupled plasma-mass spectrometry (ICP-MS). To ascertain the relationship among the water samples and their possible sources, the correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were used as classification techniques. About 10 water samples were classified into five groups using PCA. A very similar grouping was obtained using CA.

Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin

In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of heavy metal biosorption process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of process variables which are pH, biosorbent mass, and temperature, on the sorbed amount of lead were investigated through two-levels, three-factors central composite design (CCD). Same design was also utilized to obtain a training set for ANN. The results of two methodologies were compared for their predictive capabilities in terms of the coefficient of determination-R(2) and root mean square error-RMSE based on the validation data set. The results showed that the ANN model is much more accurate in prediction as compared to CCD.