Magnetite azolla impedimetric nanobiosensor for phthalic acid esters quantification.

Phthalic acid esters (PAEs) are a group of organic compounds that show vulnerability effects in different stages of human development. In this work, two sensitive and efficient impedimetric biosensors (IBs) were introduced and their interactions with four PAEs, namely dibutyl phthalate (DBP), dimethyl phthalate (DMP), di(2-ethylhexyl) phthalate (DEHP), and dicyclohexyl phthalate (DCHP), in aqueous solutions with these biosensors were separately investigated via electrochemical impedance spectroscopy (EIS). The surface of a copper electrode was modified by azolla fern dried powder (AZ) and magnetite-modified azolla nanocomposites (MAZ NCs) to form an azolla-based impedimetric biosensor (AZIB) and magnetite azolla nanocomposite-based impedimetric nanobiosensor (MAZIB), respectively. Determinations of PAEs with the designed biosensors were conducted based on their blocking effect on the biosensor surface to ferrous ions oxidation. After each impedimetric measurement, the electrode surface was covered again with the modifier. Nyquist plots were obtained and indicated that the charge-transfer resistance (RCT) values of the bare electrode, AZIB, and MAZIB without injection of PAEs were 468.8, 438.7, and 285.1 kΩ, respectively. After the separate injection of DBP, DMP, DEHP, and DCHP (3 µg L-1) on the surface of AZIB and MAZIB, RCT values were obtained as 563.9, 588.5, 548.7, and 570.1 kΩ for AZIB and 878.2, 1219.2, 754.3, and 814.7 kΩ for MAZIB, respectively. It was observed that the PAE blockers with a smaller structure provided better point-by-point coverage of the surface, which led to a bigger shift in RCT. The linear relationship between the EIS responses and each PAE concentration was investigated in the range of 0.1-1000 µg L-1. The limit of detection (LOD) and limit of quantification (LOQ) values were obtained in the ranges of 0.003-0.005 µg L-1 and 0.010-0.016 µg L-1 for AZIB and 0.008-0.009 µg L-1 and 0.027-0.031 µg L-1 for MAZIB, respectively. The results showed that these biosensors can be used to determine PAEs in real aqueous samples with good relative recoveries ranging from 93.0-97.7% (RSD < 2.58%) for AZIB and 93.3-99.3% (RSD < 2.45%) for MAZIB. The results confirmed that these impedimetric biosensors offer high sensitivity and performance for the determination of trace PAEs in aqueous samples.

Adsorption onto MWCNTs Coupled with Cloud Point Extraction for Dye Removal from Aqueous Solutions: Optimization by Experimental Design.

AIMS: The main aim of the study was to examine the feasibility and benefits of adsorption onto multi-walled carbon nanotubes (MWCNTs) coupled with cloud point extraction (CPE) for the removal of Rhodamine B (RB) from aqueous solutions. BACKGROUND: MWCNTs offer the particular features of the ideal adsorbents for the organic dyes such as hollow tubular structure and specific surface area. Nevertheless, they suffer from the drawbacks of low dispersion in the aqueous solutions and separation inconvenience from the media. Cloud point extraction, combined with the adsorption onto MWCNTs can be a promising method to overcome the problems. OBJECTIVE: In the study, adsorption onto MWCNTs coupled with CPE was applied for RB removal from aqueous solutions. The process was optimized by the response surface modeling method. Moreover, the applicability of the proposed method in the real sample analyses was investigated. METHODS: MWCNTs were used as adsorbent and Triton X-100 (TX-100) as the nonionic surfactant for CPE process. The experiments were carried out based on a Box-Behnken design (BBD) with the input variables of MWCNTs dosage (0.6-1.2 mg), solution pH (3-9), clouding time (20-40 min) and TX-100 concentration (10-20 v/v%) using 5 mg L-1 RB solutions. RESULT: Regression analyses resulted in a statistically significant quadratic model (R2=0.9718, F=24.96, p<0.0001) by which the optimum levels of the variables were predicted as: MWCNTs dosage of 0.7 mg, pH=3, clouding time of 39.9 minutes and TX-100 concentration of 19.91% (v/v). The predicted conditions were experimentally validated by achieving an RB removal of 94.24%. CONCLUSION: Based on the results, the combination of the environmentally friendly technique of CPE with adsorption onto MWCNTs allows the efficient removal of RB from water samples and the method can be effectively optimized by the response surface modeling.

Surface blocking of azolla modified copper electrode for trace determination of phthalic acid esters as the molecular barricades by differential pulse voltammetry: response surface modelling optimized biosensor.

In this work, a sensitive and efficient voltammetric biosensor was introduced for differential pulse voltammetric (DPV) determination of some phthalic acid esters (PAEs) including dibutyl phthalate (DBP), dimethyl phthalate (DMP), di(2-ethylhexyl)phthalate (DEHP) and dicyclohexyl phthalate (DCHP) in aqueous solutions. Briefly, the surface of a copper electrode was modified by azolla paste prepared using azolla powder and electroencephalography gel (EEG). The modified surface was characterized by electrochemical impedance spectroscopy (EIS), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), Brunauer-Emmett-Teller (BET) analysis and energy dispersive X-ray (EDX) methods. Determination of PAEs was conducted based on their blocking effect on the electrode surface for ferrous ion oxidation. The central composite design (CCD) was conducted to optimize the effects of four experimental parameters including the concentration of Fe2+ ions (C Fe2+ ) and supporting electrolyte (C sup. elec), solution pH and modifier/gel mass ratio on the decrease in the anodic peak current of ferrous ions as the response. Predicted optimal conditions (C Fe2+ = 319 µM, C sup. elec= 0.125 M, pH = 7.52 and modifier/gel mass ratio = 0.19) were validated by experimental checking which resulted in an error of 1.453%. At the optimum conditions, linear relationships were found between the DPV responses and PAEs concentrations and the limit of detection (LOD) and limit of quantification (LOQ) values were in the ranges of 0.2-0.4 µg L-1 and 0.5-1.0 µg L-1, respectively. Good recovery percentages ranging from 97.3 to 100.3% with RSD < 3.2% suggested the proposed method for efficient, accurate and quick determination of PAEs in real water samples.

Atom-Type-Based AI Topological Indices for Artificial Neural Network Modeling of Retention Indices of Monomethylalkanes.

In this work, a combination of Xu and atom-type-based AI topological indices (TIs) were employed for quantitative structure-retention relationship (QSRR) study of monomethylalkanes (MMAs). A total of 196 temperature-programmed gas chromatographic retention indices corresponding to all C4-C30 MMAs on OV-1 stationary phase have been used in QSRR modeling. Results of the study showed that an artificial neural network (ANN) with 4-9-1 topology and Levenberg-Marquardt training algorithm can predict the retention indices with high degree of accuracy. The statistics of root-mean-square error for the training, validation and test sets were 0.200, 0.316 and 0.215, respectively. The proposed model resulted in a maximum relative error of 0.24% suggesting the TIs as excellent alternative for estimating retention indices of MMAs. According to the obtained results, relative importance of the TIs decreased in the order of AI(-CH3)> AI(-CH2-)> AI(>CH-)> Xu showing significant role of molecular branching, steric factor and molecular size as effective structural features on retention indices of MMAs.

Simultaneous optimization of the resolution and analysis time in micellar liquid chromatography of phenyl thiohydantoin amino acids using Derringer's desirability function.

The chemometrics approach was applied for simultaneous optimization of resolution and analysis time of nine phenyl thiohydantoin amino acids in micellar liquid chromatography. Derringer's desirability function, a multi-criteria decision making method, was tested for the evaluation of the two different chromatographic performance goals. The effect of five experimental parameters on a chromatographic response function formed using two sigmoidal desirability functions was investigated. The sigmoidal functions were used to transform the optimization criteria, resolution and analysis time, into the desirability values. The factors studied were the concentration of sodium dodecyl sulfate, alkyl chain length of the alcohol used as the organic modifier, organic modifier content, mobile phase pH and temperature. The experiments were performed according to a face-centred cube response surface experimental design to map the chromatographic response surface. Then, calculated chromatographic response functions were fitted to a polynomial model. The obtained regression model was characterized by both descriptive and predictive ability (R2 = 0.988 and R(2)cv = 0.973). The model was verified, as good agreement was observed between the predicted and experimental values of the chromatographic response function in the optimal condition. Based on the results of the study, combination of response surface mapping with Derringer's desirability function allows to predict the best operating condition in micellar liquid chromatography of phenyl thiohydantoin amino acids with respect to resolution and analysis time.

Multi-criteria decision making in micellar liquid chromatographic separation of chlorophenols.

Simultaneous optimization of separation quality and analysis time of the micellar liquid chromatography of nine chlorophenol isomers was investigated. The effect on retention of three experimental parameters was studied using multivariate analysis. The factors studied were the concentration of sodium dodecyl sulfate, propanol content, and pH of the mobile phase. The experiments were performed according to the face-centered cube central composite design and the inverse form of the experimental retention times of analytes was fitted to polynomial models. The results of the analysis of variance showed that the models obtained explain over 99% of the variance observed in the chromatograms. The good predictive ability of the models was verified by high correlation coefficient (R2 > 0.99) and F ratio values for the plots of predicted cross-validated versus experimental retention times. The study showed that the use of the Pareto-Optimality method, an approach from multi-criteria decision making, allows selection of the best possible combinations of separation quality and analysis time in micellar liquid chromatography of chlorophenols.