Removal of Basic Orange 2 dye and Ni2+ from aqueous solutions using alkaline-modified nanoclay.

In the present research, the removal of Basic Orange 2 (BO2) dye using alkaline-modified clay nanoparticles was studied. To characterize the adsorbent, XRD, FTIR, FESEM, EDX, BET and BJH analyses were performed. The effect of the variables influencing the dye adsorption process such as adsorbent dose, contact time, pH, stirring rate, temperature, and initial dye concentration was investigated. Furthermore, the high efficiency of Ni2+ removal indicated that it is possible to remove both dye and metal cation under the same optimum conditions. The experimental data were analyzed by Langmuir and Freundlich isotherm models. Fitting the experimental data to Langmuir isotherm indicated that the monolayer adsorption of dye occurred at homogeneous sites. Experimental data were also analyzed with pseudo-first-order, pseudo-second-order, and intra-particle diffusion kinetic equations for kinetic modeling of the dye removal process. The adsorption results indicated that the process follows a pseudo-second-order kinetic model. The thermodynamic parameters of the dye adsorption process such as enthalpy, entropy, and Gibbs free energy changes were calculated and revealed that the adsorption process was spontaneous and endothermic in nature. The results presented the high potential of the modified nanoclay as a cost-effective adsorbent for the removal of BO2 dye and Ni2+ from aqueous medium.

Salting-out strategy for speciation of selenium in aqueous samples using centrifuge-less dispersive liquid-liquid microextraction.

The centrifuge-less dispersive liquid-liquid microextraction (DLLME) technique was used to separate selenium species in aqueous samples. According to the salting-out effect, a simple approach was used to eliminate the centrifugation step. The optimization of the independent variables was performed using chemometric methods. Under optimal conditions, this methodology was statistically validated. The linearity was between 20 and 300 µg L-1. The limit of detection and quantification were calculated 3.4 µg L-1 and 10.4 µg L-1, respectively. The values of reproducibility and repeatability were determined ≤ 9.5% and ≤ 6.4, respectively. The possibility of the method was successfully assessed by analyzing the analytes in real samples clarified satisfactory recoveries (98.1-101.4% for Se (IV) and 98.4-101.5% for Se (VI)).

Validation of chemometric-assisted single-drop microextraction based on sustainable solvents to analyze polyaromatic hydrocarbons in water samples.

Chemometric and statistic approaches were applied to optimize and validate headspace-single drop microextraction-based deep eutectic solvents combined with gas chromatography-mass spectrometry. The proposed technique was used for the pre-concentration, separation, and detection of polyaromatic hydrocarbons in aqueous samples. The efficacy of the independent variables on the extraction efficiency was studied via chemometric methods in two steps. The method exhibits good linearity for the analytes (R2 ≥ 0.9989). Under optimal conditions, the analytical signal was linear in the range of 0.01-50 µg L-1. The limit of detection and limit of quantitation were evaluated in the concentration range of 0.003-0.012 and 0.009-0.049 µg L-1, respectively. The precision consisting of repeatability and reproducibility were assessed by estimating the relative standard deviation (RSD%), and their values were found to be lower than 7.1% and 11.7%, respectively. Consequently, the proposed procedure was used to extract and analyze 19 polyaromatic hydrocarbons in real aqueous samples, which demonstrated satisfactory recoveries (94.40-105.98%).

Deep eutectic solvent-based headspace single-drop microextraction of polycyclic aromatic hydrocarbons in aqueous samples.

An improved deep eutectic solvent (DES)-based headspace single-drop microextraction procedure has been developed as a green procedure for gas chromatography-mass spectrometric analysis of polycyclic aromatic hydrocarbons (PAHs) in aqueous samples. The stability of the micro-drop was significantly improved using a DES as an extraction phase and a bell-shaped tube as a supporter. These strategies helped to perform the extraction process in higher temperatures and stirring rates. Finally, the back-extraction of the analytes into a proper solvent that is compatible with the chromatography system was applied. The efficacy of the independent variables on the extraction efficiency was evaluated via chemometric methods in two steps. The best result was obtained with choline chloride-oxalic acid at the molar ratio of 1:2, a stirring speed of 2000 rpm for 10 min as well as a sample temperature of 50 °C and with ionic strength prepared by using a 10% (w/v) NaCl. The method indicated a good linearity for the analytes (R2≥0.9989). Under optimal conditions, the analytical signal was linear in the range of 0.01-50 µg L-1. Limit of detection (LOD) and limit of quantification (LOQ) were evaluated at the concentration levels of 0.003-0.012 and 0.009-0.049 µg L-1, respectively. Intraday and interday precision for all targeted compounds was less than 7.2% and 11.3%, respectively. Consequently, the proposed procedure was efficiently applied to extract and analyze the 16 target compounds in real aqueous samples representing satisfactory recoveries (94.40-105.98%).

Prediction of Kovats retention indices of some aliphatic aldehydes and ketones on some stationary phases at different temperatures using artificial neural network.

In this work, the Kovats retention indices of aliphatic ketones and aldehydes on four stationary phases at different temperatures are predicted. The data set consists of retention indices of 35 aldehydes and ketones on HP-1, HP-50, DB-210, and HP-Innowax stationary phase. The molecular descriptors that appear in this model are: path one connectivity index, fractional atomic charge weighted by partial positive surface area, and dipole moment, which are selected by stepwise multiple linear regression (MLR). The selected descriptors encode steric and electronic aspects of molecules. These descriptors, together with column temperature, are used as inputs of the constructed artificial neural network (ANN). The optimized network has 4-3-4 topology, in which its outputs are retention indices of molecules at four stationary phases at the desired temperature. Comparison between statistical results calculated for MLR and ANN models reveals that all statistics have improved considerably in the case of the ANN model. The improved statistics for the ANN would suggest the existence of a nonlinear relation between selected molecular descriptors and their retention in gas chromatography. Also, the simultaneous prediction of retention indices for aldehydes and ketones at four stationary phases at different temperatures using only three molecular descriptors shows the capability of the obtained ANN model.