Photocatalytic degradation of ciprofloxacin by MMT/CuFe2O4 nanocomposite: characteristics, response surface methodology, and toxicity analyses.

The photocatalytic degradation of ciprofloxacin (CIP) was examined by loading spinel ferrite copper (CuFe2O4) nanoparticles onto montmorillonite (MMT) under irradiation using UV light. The laboratory parameters were optimized using response surface methodology (RSM), and maximum efficiency (83.75%) was achieved at a pH of 3, CIP concentration of 32.5 mg/L, MMT/CuFe2O4 dose of 0.78 g/L, and irradiation time of 47.50 min. During the photocatalysis process, the experiments on radical trapping demonstrated the generation of hydroxyls (•OH), superoxide (•O2-) radical, electrons (e-), and holes (h+). A low rate drop (below 10%) in the CIP degradation during the six consecutive reaction cycles corroborated the remarkable recyclability and stability of the MMT/CuFe2O4. The acute toxicity of the treated solution was determined using Daphnia Magna, by applying photocatalysis, which was indicative of a marked decline in the toxicity. Comparing the findings of the degradation using UV and the degradation process using visible light represented results with close resemblance to each other at the end of the reaction time. Besides, under UV and visible light, the particles in reactor are easily activated when the pollutant mineralization exceeded 80%.

Role of immobilised Chlorophyta algae in form of calcium alginate beads for the removal of phenol: isotherm, kinetic and thermodynamic study.

In this work, sodium alginate-immobilised Chlorophyta algae were evaluated for phenol uptake. The algae/alginate bead (AAB) characteristics were analysed by means of BET-BJH, FTIR, and SEM-EDX methods, while the adsorption performance of AABs with respect to phenol removal was investigated using batch studies. The parameters found to affect the biosorption capacity of AABs included pH, contact time, initial phenol concentration, adsorbent dosage, stirring rate, particle size, and temperature, with the optimal operating variables identified as a pH of 6, an initial phenol concentration of 50 mg/L, AAB dosage of 5 g/L, and a 200 rpm stirring rate. The adsorption process in such cases reached equilibrium within 120 min, demonstrating a maximum phenol elimination capacity of 9.56 mg/g at 30 °C. The isotherm and kinetic models used to determine this were evaluated using the Chi-square test (X2), the coefficient of determination (R2), and the value of equilibrium capacity, with results that revealed that the Freundlich isotherm provides the best fit for the relevant equilibrium data, as shown by its high R2 value (0.96) and low X2 value (1.16135); the theoretical data produced by that model were thus closer to the experimental data than that from the Langmuir model. Kinetic analysis showed that the phenol adsorption followed a pseudo-second-order kinetic model. The thermodynamic parameters were thus explored, revealing that the phenol biosorption process is based on spontaneous physisorption with an exothermic reaction due to negative (ΔG°) and (ΔH°) values. The low cost, natural origin, biodegradability, and eco-friendliness of algae/alginate bead sorbents also make them ideally suited for phenol removal in aqueous solutions.

Bio-synthesis of TiO2 using grape leaves extract and its application for photocatalytic degradation of ibuprofen from aqueous solution.

ABSTRACTIn this study, titanium oxide nanoparticle was fabricated using the extract of grape leaves (referred asGL-TiO2 NPs), using the green synthesis process, and then explores its ability for photodegradation of ibuprofen (IBU) under UV light in the batch system. UV-Vis, BET, FTIR, XRD, and SEM-EDX tests were made to identify the catalyst's structure and shape. Moreover, the effects of different operating parameters, specifically pH of (3, 5, 7, and 9), IBU concentration (10, 20, 40, and 80) mg/L, GL-TiO2 concentrations (15, 30, and 60) mg/L, H2O2 (100, 300, and 500) mg/L, and contact time were studied. According to the results, the synthetic TiO2 NPs have a spherical shape and 39.608 m2/g of BET surface area. In addition, the findings showed that the removal efficiency reached 92.32% under optimum conditions of 5, 10, 30, 300 mg/L, and 150 min, respectively. In addition, the reaction followed a first-order kinetics model with R2 > 97. According to the finding of this study, GL-TiO2 NPs has an acceptable efficiency in the elimination of IBU, as their relatively simple synthesis, could be a suitable catalyst for the degradation and elimination of pharmaceutical residues.

The biosorption of reactive red dye onto orange peel waste: a study on the isotherm and kinetic processes and sensitivity analysis using the artificial neural network approach.

The agricultural waste of orange peels (OPs) was utilized as a cheap biosorbent and then tested for its ability to treat the reactive red (RR) dye wastewater. Several experiments were done to get the equilibrium isotherm and kinetic-relevant data. In addition, several experimental factors such as solution pH, temperature, contact time, and initial RR dye concentration were studied, in light of their impact on the biosorption process. The utilized isotherm and kinetic models were evaluated by using the chi-square test and coefficient of determination parameters for their representation of real data. In addition, the obtained data of their biosorption capacities, at different conditions, were modeled by the artificial neural network (ANN) approach. The results of the isotherm study revealed that the experimental data can be best accounted by both the Langmuir and Temkin models, demonstrating that the RR molecules were sorbed to two or more different types of biosorption sites of OP. The kinetic study for determining the characteristics of the rate of diffusion demonstrated that the intraparticle diffusion process was not the sole rate-limiting step in the biosorption of the RR dye-OP couple. Furthermore, the biosorption process was chemisorption in nature, as the pseudo-second-order reaction proved to be the best representative model for the kinetic data. The outcome of modeling also assumed that using the ANN tool was useful to reproduce the data again and foretell the manner in which biosorption behaved. According to the results of the Langmuir model, it was found that the maximum OP uptake for the biosorption of the RR dye was up to 82 mg/g, observed at optimized values of the experimental parameters. Such prior results highlight that OP is an effective agent of biosorption in the elimination of RR dyes from polluted solutions, moreover, in a cost-effective manner.

Adsorptive performance of a mixture of three nonliving algae classes for nickel remediation in synthesized wastewater.

PURPOSE: The present study provided a comprehensive description regarding the application of a mixture of three nonliving classes of algae as a promising and inexpensive biosorbent for removing toxic nickel (Ni(II)) ions from the aqueous medium. METHODS: The biosorption process was tested by varying several experimental parameters such as pH (2-8), contaminant concentration (20-300 mg/L), biosorbent content (0.2-2 g/100 mL), and temperature (20-40 °C). In addition, the competition effects of the presence of Pb(II), Cu(II), and Zn(II) ions on the Ni(II) removal efficiency was studied by varying their concentrations from 30 to 40 mg/L. RESULTS: The microscopic analysis of algae demonstrated that the used biosorbent consisted mainly of Chrysophyta (80%), Chlorophyte (14%), and Cyanophyta (6%). Results demonstrated that these environmental parameters influenced the removal efficiency with a different degree and there was no stable effects rank at conditions under examination. FT-IR and SEM analysis revealed that the biosorbent surface consists of many strong and active groups of negative valences such as hydroxyl and carboxyl groups, thus exhibiting several morphological properties of interest. Further, it was found that the Temkin model best fitted the isotherm biosorption data. The kinetic study showed that the Ni(II) biosorption was rapid within first 20 min of reaction time, thereby following a pseudo-second-order model, which in turn demonstrated a chemisorption process of Ni(II) ions reaction with the biosorbent binding sites. Also, the thermodynamic study suggested that the biosorption process of Ni(II) onto algal biomass was a spontaneous and endothermic in nature. The maximum uptake of Ni(II) was 9.848 mg/g under optimized conditions and neutral environment. CONCLUSIONS: Thus, this significant finding suggested a favorable and eco-friendly treatment mechanism for removal of Ni(II) ions from aqueous medium via biosorption onto the used mixture of nonliving algal biomass.

The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%.