Arsenate removal from aqueous solutions using micellar-enhanced ultrafiltration.

In this study, arsenate (As-V) removal using micellar enhanced ultrafiltration (MEUF) modified by cationic surfactants was studied by a dead-end polyacrylonitrile (PAN) membrane apparatus. The UF membrane has been produced by a phase inversion process. The prepared membrane was characterized and analyzed for morphology and membrane properties. The influence of operating parameters such as initial concentrations of As-V, surfactants, pH, membrane thickness, and co-existing anions on the removal of As-V, surfactant rejection, and permeate flux have been studied. The experimental results show that from the two different cationic surfactants used the CPC (cetyl-pyridinium chloride) efficiency (91.7%) was higher than that of HTAB (hexadecyltrimethyl-ammonium bromide) (83.7%). The highest As-V removal was 100%, and was achieved using initial feed concentrations of 100-1000 µg/L, at pH 7 with a membrane thickness of 150 µm in a dead-end filtration system. This efficiency for As-V removal was similar to that obtained using a cross-flow system. Nevertheless, this flux reduction was less than the reduction achieved in the dead-end filtration process. The PAN fabricated membrane in comparison to the RO and NF processes selectively removed the arsenic and the anions, in the water taken from the well, and had no substantial effect on the cations.

Effects of doping zinc oxide nanoparticles with transition metals (Ag, Cu, Mn) on photocatalytic degradation of Direct Blue 15 dye under UV and visible light irradiation.

BACKGROUND: Azo dyes represent the most commonly used group of dyes in the textile industry. These organic dyes are mainly resistant to biodegradation and may exhibit toxic and carcinogenic properties. The purpose of this study was to investigate the effects of doping zinc oxide (ZnO) nanoparticles (NPs) with transition metals (silver, manganese, and copper) on the photocatalytic efficiency of ZnO NPs in the removal of Direct Blue 15 dye from aqueous environments under ultraviolet (UV) radiation and visible light irradiation. METHODS: One or two metals were used for doping the NPs. In total, seven types of undoped and transition metal-doped NPs were synthesized using the thermal solvent method with ZnO precursors and transition metal salts. The characteristics of the synthesized NPs were determined based on the scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), atomic force microscopy (AFM), and zeta potential measurements. RESULTS: The produced ZnO NPs did not exhibit any particular photocatalytic activities under UV radiation and visible light irradiation. The highest removal efficiency under UV radiation was about 74% in the presence of silver-doped ZnO NPs, while the maximum efficiency under visible light was 70% in the presence of copper-doped ZnO NPs. The lowest removal efficiency was related to pure ZnO, which was 18.4% and 14.6% under UV and visible light irradiation, respectively. Although the efficiency of dye removal under visible light was not high compared to UV radiation, this efficiency was noteworthy in terms of both practical and economic aspects since it was achieved without the presence of ultraviolet radiation. CONCLUSIONS: The synthesis of transition metal-doped ZnO nanophotocatalysts (with one or two metals) under UV radiation or visible light irradiation could be used as an efficient and promising technology for the photocatalytic removal of Direct Blue 15 dye from aqueous environments.

Application of modified electrospun nanofiber membranes with α-Fe2O3 nanoparticles in arsenate removal from aqueous media.

In the present study, electrospun nanofiber membranes (ENMs) of polyacrylonitrile (PAN) were modified by dispersing α-Fe2O3 nanoparticles, synthesized using a thermal solvent process, in a PAN solution. The morphology and physiochemical properties of the prepared ENMs and the α-Fe2O3 were characterized using FESEM, EDX, BET, XRD, FTIR, porosity, and contact angle measurement. XPS was used to investigate the interaction of ENM with arsenate (As(V)) during the adsorption. Moreover, the effect of pH, the equilibrium isotherm, and the kinetics were investigated in batch experiments. The Langmuir isotherm best correlated the experimental results, indicating monolayer adsorption on ENMs, and the kinetics was best fitted, R2 > 0.99, by the pseudo-second-order model. In addition, the effects of certain conditions on the filtration performance were examined, such as feed concentration and transmembrane pressure (TMP). By passing sodium hydroxide (0.1 M) for 20 min, the membrane was regenerated. The increase in TMP, along with the presence of co-ions including chloride, nitrate, and sulfate, had negative impacts on the removal of As(V). The results show that the modified ENMs with α-Fe2O3 nanoparticles are applicable for As(V) ion removal and possibly for eliminating other heavy metals from aqueous media.

High-flux ultrafiltration membrane based on electrospun polyacrylonitrile nanofibrous scaffolds for arsenate removal from aqueous solutions.

Arsenic contamination in drinking water is a serious problem worldwide. In this study, to remove arsenate from contaminated water, a new thin-film composite (TFC) membrane was fabricated and tested. This membrane was composed of an electrospun nanofibrous scaffold, a polyethylene terephthalate (PET) substrate as support, and a polyacrylonitrile (PAN) coating layer. To effectively reject arsenate ions, cetylpyridinium chloride (CPC) pretreatment was used. For evaluating the performance of TFC membrane, its flux and contaminant rejection were compared to a conventional ultrafiltration (UF) membrane. Due to high porosity, the TFC membrane showed a flux, which was 172-520% higher than the UF membrane. In addition, The TFC membrane was 1.1-1.3 times more efficient in rejecting arsenate ions than the UF membrane.

Electrocatalytic activity of manganese oxide nanosphere immobilized onto deoxyribonucleic acid modified electrode: Application to determine environmental pollutant thiourea at natural pH.

Based on immobilizing manganese oxide nanospheres (MnOxNsph)/deoxyribonucleic (DNA) on glassy carbon electrode (MnOxNsph/DNA/GCE), a new electrochemical biosensor for the detection of thiourea (TU) was fabricated. In order to prepare DNA template, cyclic voltammetry (CV) method was used. Scanning electron microscopy (SEM) was used for characterization of MnOx/DNA. Electrochemical impedance spectroscopy was utilized to confirm the successful stepwise assembly procedure of the biosensor. The electrocatalytical behaviors of the biosensor were also investigated by cyclic voltammetry and differential pulse voltammetry (DPV). The results showed that MnOxNsph/DNA exhibited a remarkable electrocatalytic activity for the oxidation of TU under optimal conditions. The linear range, detection of limit and sensitivity were calculated for oxidation peaks (OX1 and OX2). This electrode demonstrated many advantages such as high sensitivity, low detection of limit, excellent catalytic activity at natural pH values, remarkable antifouling property toward TU and its oxidation product for the two oxidation peaks.

Super high removal capacities of heavy metals (Pb2+ and Cu2+) using CNT dendrimer.

This research demonstrates the capability of carbon nanotubes (CNT) modified with four generations of poly-amidoamine dendrimer (PAMAM, G4) to remove Cu2+ and Pb2+ heavy metals from aqueous solution in single and binary component systems. Uniquely high adsorption capacities for copper and lead, which are 3333 and 4870mg/g respectively, were achieved. FTIR, H1 NMR, Zeta potential, SEM and TEM techniques were employed for characterizing the synthetic nanocomposite and indicated that the dendrimer functionalized CNTs have been synthesized. The effects of several parameters including initial metal ion concentration, solution pH and the nanocomposite dosage were studied. The experimental data were analyzed by the Langmuir and Freundlich isotherms and the pseudo-first order and pseudo-second order kinetics models. The maximum adsorption occurred at pH=7. The adsorption process for Cu2+ and Pb2+ in single and binary component systems fit the Langmuir and extended Langmuir models respectively. This study also tested the kinetic sorption of the metals on PAMAM/CNT in single and binary component metal systems at various metal ions concentrations. The results showed that PAMAM/CNT nanocomposite was a super-adsorbent, able to uptake uniquely large quantities of heavy metal from single and binary component liquid phase.

Daily Fluoride Intake from Iranian Green Tea: Evaluation of Various Flavorings on Fluoride Release.

With increased awareness of the health benefits of the compounds in green tea, especially polyphenols, its consumption is rising. The main purpose of this study is to determine the effect of different additives on the released fluoride into tea liquor and also daily fluoride intake. The concentrations of fluoride, nitrate, sulfate, and chloride were measured in 15 different flavored green teas (Refah-Lahijan). The fluoride and other anion concentrations were measured by ion chromatography method. The data were analyzed with Statistical Package for the Social Sciences version 16.0. The results showed that the minimum and maximum concentrations of fluoride in the green tea infusions were 0.162 mg/L (cinnamon-flavored green tea) and 3.29 mg/L (bagged peach-flavored green tea), respectively. The mean concentration of fluoride in the green tea leaves was 52 mg/kg, and approximately 89% of the fluoride was released from the green tea leaves into the infusions after brewing. The fluoride concentrations varied significantly among the examined green teas (P < 0.05). However, the additives had no significant effect on the fluoride release into the infusions (P > 0.05). Finally, drinking of the studied green teas cannot make a significant contribution to the daily dietary intake of F for consumers.

Photocatalytic degradation of humic substances in aqueous solution using Cu-doped ZnO nanoparticles under natural sunlight irradiation.

In this study, Cu-doped ZnO nanoparticles were investigated as an efficient synthesized catalyst for photodegradation of humic substances in aqueous solution under natural sunlight irradiation. Cu-doped ZnO nanocatalyst was prepared through mild hydrothermal method and was characterized using FT-IR, powder XRD and SEM techniques. The effect of operating parameters such as doping ratio, initial pH, catalyst dosage, initial concentrations of humic substances and sunlight illuminance were studied on humic substances degradation efficiency. The results of characterization analyses of samples confirmed the proper synthesis of Cu-doped ZnO nanocatalyst. The experimental results indicated the highest degradation efficiency of HS (99.2%) observed using 1.5% Cu-doped ZnO nanoparticles at reaction time of 120 min. Photocatalytic degradation efficiency of HS in a neutral and acidic pH was much higher than that at alkaline pH. Photocatalytic degradation of HS was enhanced with increasing the catalyst dosage and sunlight illuminance, while increasing the initial HS concentration led to decrease in the degradation efficiency of HS. Conclusively, Cu-doped ZnO nanoparticles can be used as a promising and efficient catalyst for degradation of HS under natural sunlight irradiation.

Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.

Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.