Dispersive nano solid material-ultrasound assisted microextraction as a novel method for extraction and determination of bendiocarb and promecarb: response surface methodology.

A new extraction method, based on Dispersive Nano-Solid material-Ultrasound Assisted Micro-Extraction (DNSUAME), was used for the preconcentration of the bendiocarb and promecarb pesticides in the water samples prior to high performance liquid chromatography (HPLC). The properties of NiZnS nanomaterial loaded on activated carbon (NiZnS-AC) are characterized by FT-IR, TEM, and BET. This novel nanomaterial showed great adsorptive ability towards the bendiocarb and promecarb pesticides. The effective variables such as the amount of adsorbent (mg: NiZnS-AC), the pH and ionic strength of sample solution, the vortex and ultrasonic time (min), the ultrasonic temperature (°C), and desorption volume (mL) are investigated by screening 2(7-4) experiments of Plackett-Burman (PB) design. The important variables optimized by using a central composite design (CCD) were combined by a desirability function (DF). At optimum conditions, the method has linear response over 0.0033-10 µg mL(-1) with detection limit between 0.0010 and 0.0015 µg mL(-1) with relative standard deviations (RSDs) less than 5.5% (n=3). The method has been successfully applied for the determination of the bendiocarb and promecarb pesticides in the water samples.

Studies on the adsorption behavior of trace amounts of 90Sr2+, 140La3+, 60Co2+, Ni2+ and Zr4+ cations on synthesized inorganic ion exchangers.

Three inorganic ion exchangers namely potassium zinc hexacyanoferrate(II) (PZF), magnesium oxide-polyacrylonitrile composite (MgO-PAN) and ammonium molybdophosphate (AMP) were synthesized. The physicochemical properties of these ion exchangers were determined using different techniques including inductively coupled plasma (ICP), CHNSO elemental analysis, infrared spectroscopy (IR), X-ray diffraction (XRD), thermogravimetric (TGA) and pH - titration curve analysis. The solubility of the synthesized ion exchangers in different acidic and alkaline media, their thermal stability and the effect of gamma irradiation were investigated. It was observed that the exchange capacity of the ion exchangers depend upon the pH value of the solution used. Furthermore, the adsorption of (90)Sr(2+), (140)La(3+), (60)Co(2+) and the distribution coefficient of these ion exchangers for Ni(2+)and Zr(4+) were studied. The effect of parameters such as pH and contact time on the adsorption was also investigated and the optimum conditions for separation of these ions were determined.

Quantitative structure-property relationship study of retention time of some pesticides in gas chromatography.

A quantitative structure-property relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques is carried out to investigate the retention time behavior of some pesticides on the DB-5ms fused-silica column in gas chromatography. Five descriptors selected in the MLR model are: first component WHIM index (E1v), highest eigenvalue n.7 of burden matrix / weighted by atomic van der waals volume (BEHv7); average connectivity index Chi-2 (X2a), 3D-MoRSE signal 23 weighted by atomic Sanderson electronegativity (MoR23m); and principal moments of inertia B (PMIB). A 5-5-1 ANN is also generated to investigate the retention behavior of described pesticides using the same descriptors MLR model as inputs. The statistical parameters derived from MLR and ANN for all molecules are: correlation coefficient (R)(MLR) = 0.929, standard errors (SE)(MLR) = 3.452, R(ANN) = 0.943, and SE(ANN) = 3.112. The mean of relative errors between the MLR and ANN calculated and the experimental values of the retention times for the prediction set are 13.8% and 9.04%, respectively. The correlation coefficient and standard error of ANN model compared with MLR models showed the superiority of ANNs over regression models. This is partly due to the fact that ANN considers the interaction between different parameters as well as nonlinear relation.

Simultaneous optimization of resolution and analysis time in mixed micellar liquid chromatography of coumarins by use of a utility function.

A chemometrics approach has been used to optimize the separation of eight coumarin compounds by mixed micellar liquid chromatography. A utility function, a multi-criterion decision-making (MCDM) method, was tested for evaluation of two different measures of chromatographic performance (resolution and analysis time). The effect of six experimental parameters on a chromatographic response function (CRF) was investigated. The factors studied were the concentrations of SDS and Brij-35, alkyl-chain length of the alcohol used as organic modifier, organic modifier concentration, mobile phase pH, and temperature. The experiments were performed according to a face-centered cube response-surface experimental design. For evaluation of the chromatograms a simple linear response function was used which expressed as a summation of two optimization criteria, resolution and analysis time. Then calculated CRF values were fitted to a polynomial model to correlate the CRF values with the variables and their interactions. The regression model obtained was characterized by both its descriptive and predictive ability (R=0.963 and R2cv=0.861) and used, by means of a grid-search algorithm, to optimize the chromatographic conditions. Experiments performed under the optimum conditions predicted by the model produced a chromatogram of high quality. The model was also verified by the good agreement observed between predicted and experimental values of the chromatographic response function under the optimum conditions.