Removal of Basic Orange 2 dye and Ni 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.

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.

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.

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.

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).