Synthesis and characterization of antibacterial chromium iron oxide nanoparticle‐loaded activated carbon for ultrasound‐assisted wastewater treatment

The aim of this study was to evaluate the surface adsorption capacity of CrFeO₃ nanoparticle‐loaded activated carbon (CrFeO₃‐NPs–AC) for the removal of a cationic dye (methyl violet, MV). CrFeO₃‐NPs were hydrothermally synthesized and loaded on AC followed by characterization using X‐ray diffraction, field‐emission scanning electron microscopy and energy‐dispersive and Fourier transform infrared spectroscopies. The CrFeO₃‐NPs were tested for in vitro antibacterial activities against Gram‐positive (Staphylococcus aureus) and Gram‐negative (Pseudomonas aeruginosa) bacteria. Minimum inhibitory and minimum bactericidal concentrations of CrFeO₃‐NPs–AC were obtained to be 50 and 100 μg ml^?1, respectively, against S. aureus and 25 and 50 μg ml^?1 against P. aeruginosa. These results indicated the antibacterial properties of CrFeO₃‐NPs–AC. To investigate the adsorption process, several systematic experiments were designed by varying parameters such as adsorbent mass, pH, initial MV concentration and sonication time. The adsorption process was modelled and the optimal conditions were determined to be 0.013 g, 7.4, 15 mg l^?1 and 8 min for adsorbent mass, pH, MV concentration and sonication time, respectively. The real experimental data were found to be efficiently explained by response surface methodology and genetic algorithm model. Kinetic studies for MV adsorption showed rapid sorption dynamics described by a second‐order kinetic model, suggesting a chemisorption mechanism. Then, the experimental equilibrium data obtained at various concentrations of MV and adsorbent masses were fitted to conventional Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherm models. Dye adsorption equilibrium data were fitted well to the Langmuir isotherm. From the Langmuir model, the maximum monolayer capacity was calculated to be 65.67 mg g^?1 at optimum adsorbent mass.     

Application of nanostructure ZnLI2 complex in construction of optical pH sensor

This is for the first time that application of complex nanostructure is reported as pH indicator in PVC matrix. This new optical pH sensor was constructed based on incorporation of ZnLI₂ complex nanostructure in PVC matrix. The synthesized nanostructure ZnLI₂ complex was characterized by SEM and XRD technique. The membrane solution was speared on the glass plate to provide thin film and the membrane surface morphology was investigated via field emission scanning microscope (FE‐SEM) technique. Central composite design (CCD) combined with desirability function (DF) was applied to find the best experimental composition of membrane providing the highest absorbance. These conditions were found in correspondence with 3 mg of pH indicator, 3 mg of ionic additive and 1.5 mg/mg of DBP/PVC weight ratio. Under optimum conditions, the proposed pH sensor has two linear working ranges of 4 ‐ 8 at 393 nm (R² = 0.9897) and 5 ‐ 8 (R² = 0.9982) at 570 nm with response time of 4 min. The pK_a of proposed pH optical sensor was calculated through three methods that found to be 5.63. The present optical sensor shows stability after 2 months without any significant divergence in response properties (less than 5% RSD). Furthermore, current pH optode was exhibited good repeatability (RSD = 1.14%) as well as reproducibility (RSD = 4.06%). No significant variation was observed on sensor response with increasing the ionic strength in the range of 0.0–0.5 M of sodium chloride. All above features indicated that the proposed sensor can be successfully used for detection of pH in solutions with different ionic strength.     

Synthesis of CuS and ZnO/Zn(OH)2 nanoparticles and their evaluation for in vitro antibacterial and antifungal activities

In this study, the copper sulfide nanoparticles (CuS‐NPs) and the zinc oxide/zinc hydroxide nanoparticles ((ZnO/Zn(OH)₂‐NPs) were synthesized by a simple and low‐cost method, and the synthesized nanoparticles were characterized and identified by UV–Vis, field emission scanning electron microscopy (FE‐SEM), transmission electron microscopy (TEM) and X‐ray diffraction (XRD). The antimicrobial activity of the CuS‐NPs and the ZnO/Zn(OH)₂‐NPs were examined by broth dilution to determine the minimal inhibitory concentration (MIC) of antibacterial agent required to inhibit the growth of a pathogen and the minimum bactericidal concentration (MBC) required to kill a particular bacterium. Agar disc diffusion method was used to determine the zone of inhibition. The nanoparticles demonstrated potent antibacterial activity against Klebsiella pneumonia (ATCC 1827), Acinetobacter baumannii (ATCC 150504), Escherichia coli (ATCC 33218) and Staphylococcus aureus (ATCC 25293). Antifungal activity against Aspergillus oryzae (PTCC 5164) was also obtained. The data obtained from antimicrobial activities by broth dilution and agar disc diffusion methods exhibited the CuS‐NPs were more effective than the ZnO/Zn(OH)₂‐NPs. A good correlation was observed between the data obtained by both methods.     

Construction of molecularly imprinted nanoparticles by employing ultrasound waves for selective determination of doxepin from human plasma samples: Modeling and optimization

In this work, molecularly imprinted nanoparticles (MINPs) were applied as selective adsorbent for ultrasound‐assisted micro‐solid‐phase extraction (UAMSPE) of doxepin (DP) from human plasma samples, which was then cleaned up, pre‐concentrated and subjected to HPLC. The MINPs were synthesized based on a non‐covalent approach by precipitation polymerization utilizing methacrylic acid and styrene as functional monomers, DP as template, ethylene glycol dimethacrylate as cross‐linker and 2,2‐azobisisobutyronitrile (AIBN) as initiator. The obtained MINPs were characterized by Fourier transform‐infrared and field emission scanning electron microscopy. Factors influencing the efficiency of UAMSPE such as sonication time, volume of eluent solvent and amount of sorbent were investigated using a central composite design and the optimal points were identified as 4 min of sonication time, 380 μL of eluent solvent and 30 mg of sorbent. Under optimized conditions, the proposed method has linear responses in the range of 0.2–2000 ng mL^–1, with a satisfactory limit of detection of 0.04 ng mL^–1 and limit of quantification of 0.11 ng mL^–1.     

Preconcentration/Separation of Some Metal Ions Using Sodium Dodecyl Sulfate Coated Alumina Modified with Bis(5‐bromo‐2‐hydroxy‐benzaldehyde)‐2‐methyl‐1,5‐pentane Diimine (BBHBPDI) Prior to Their Flame‐AAS Determination

Preconcentration/separation of Co(II), Fe(III), Pb(II), Cr(III), Cu(II) and Cd(II) ions using bis(5‐bromo‐2‐hydroxy‐benzaldehyde)‐2‐methyl‐1,5‐pentane diimine (BBHBPDI) on SDS coated alumina has been reported. The influences of the analytical parameters including pH, ligand and SDS amount, type and concentration of eluent and sample volume on metal ions recoveries were investigated. At optimum values of all variables the relative standard deviation are between 2.5–2.7 and preconcentration factor was 375, while recoveries for all understudy metal ions are higher than 95%, determination limits are between 1.5–2.7. The method has been successfully applied to determination of Co(II), Fe(III), Pb(II), Cr(III), Cu(II) and Cd(II) ions content in some real samples.     

Simultaneous determination of thiocyanate and salicylate by a combined UV-spectrophotometric detection principal component artificial neural network

A modified principle component artificial neural network (PC-ANN) model is developed for simultaneous determination of thiocyanate and salycilate concentration after passing through the bulk of a liquid membrane by tri-phenyl benzyl phosphonium chloride. All calibration, and test samples data were obtained using UV-Vis spectrophotometer. In this way, a modified PC-ANN consisting of three layers of nodes was trained by combination of Bayesian-Levenberg-Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC-ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 x 10(-4) mol.l(-1) up to 5.0 x 10(-4) mol.l(-1) with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R2= 0.998 or greater). Results obtained with modified trained PC-ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC-ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).