The performance of mesoporous magnetite zeolite nanocomposite in removing dimethyl phthalate from aquatic environments

Simple Preparation of the CuO•Fe3O4/Silica Composite from Rice Husk for Enhancing Fenton-Like Catalytic Degradation of Tartrazine in a Wide pH Range

SiO2 was prepared from rice husk (RH) with the assistance of cetrimonium bromide (CTAB), and the CuO•Fe3O4/SiO2 composite was prepared by a simple coprecipitation method to enhance the Fenton-like degradation of dyes in a wide pH range. SiO2 was a mesoporous material with a relatively large surface area of 496.4 m2/g and a highly relative pore volume of 1.154 cm3/g. The Fe3O4 and CuO particles with the size of 20–50 nm were well dispersed in the composite, making the composite tighter and causing the disappearance of large pores in the range of 20–55 nm. The surface area and pore volume of the composite were reduced to 248.6 m2/g and 0.420 cm3/g, respectively. Fe3O4/SiO2 and Fe3O4 samples only exhibited high catalytic activity in an acidic medium, while the CuO•Fe3O4/SiO2 composite could effectively work in a wide pH range of 3–7. Besides, the effects of reaction conditions such as catalyst dosage, H2O2 concentration, and initial dye concentration on the catalytic performance of the composite were studied. The optimal conditions for the degradation of dye were tartrazine (TA) concentration of 50 mg/L, dosage catalyst of 0.5 g/L, H2O2 concentration of 120 mM, and pH 5. The CuO•Fe3O4/SiO2 composite reached the highest activity at pH 5, showing a degradation efficiency (DE) of 93.3% and a reaction rate of 0.061 min−1. The reusability of the catalyst was investigated by cyclic experiments. The DE of the 3rd reuse remained at 55.1%, equivalent to 93.5% of the first use. The catalytic mechanism for the Fenton system has also been proposed.

Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD $\text{concentration}\left(C_{\text{o}}\right)=100$  mg/L, mixing $\text{rate}=200$  rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal ( $R^2$ : 0.972, Adj- $R^2$ : 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as $\text{pH}>\text{adsorbent}\text{dosage}>\text{time}\approx \text{stirring}\text{speed}>C_{\text{o}}$ . A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process.

Soil microbiomes divergently respond to heavy metals and polycyclic aromatic hydrocarbons in contaminated industrial sites

Contaminated sites from electronic waste (e-waste) dismantling and coking plants feature high concentrations of heavy metals (HMs) and/or polycyclic aromatic hydrocarbons (PAHs) in soil. Mixed contamination (HMs + PAHs) hinders land reclamation and affects the microbial diversity and function of soil microbiomes. In this study, we analyzed HM and PAH contamination from an e-waste dismantling plant and a coking plant and evaluated the influences of HM and PAH contamination on soil microbiomes. It was noticed that HMs and PAHs were found in all sites, although the major contaminants of the e-waste dismantling plant site were HMs (such as Cu at 5,947.58 ± 433.44 mg kg^-1, Zn at 4,961.38 ± 436.51 mg kg^-1, and Mn at 2,379.07 ± 227.46 mg kg^-1), and the major contaminants of the coking plant site were PAHs (such as fluorene at 11,740.06 ± 620.1 mg kg^-1, acenaphthylene at 211.69 ± 7.04 mg kg^-1, and pyrene at 183.14 ± 18.89 mg kg^-1). The microbiomes (diversity and abundance) of all sites were determined via high-throughput sequencing of 16S rRNA genes, and redundancy analysis was conducted to investigate the relations between soil microbiomes and contaminants. The results showed that the microbiomes of the contaminated sites divergently responded to HMs and PAHs. The abundances of the bacterial genera Sulfuritalea, Pseudomonas, and Sphingobium were positively related to PAHs, while the abundances of the bacterial genera Bryobacter, Nitrospira, and Steroidobacter were positively related to HMs. This study promotes an understanding of how soil microbiomes respond to single and mixed contamination with HMs and PAHs.

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

Evaluating the performance of coupled MFC-MEC with graphite felt/MWCNTs polyscale electrode in landfill leachate treatment, and bioelectricity and biogas production

Purpose

A bioelectricity producing system was configured by connecting to a microbial electrolysis cell producing hydrogen, in which both systems were without mediator, to treatment the landfill leachate of the and generate bioelectricity and hydrogen.

Methods

The anode electrode was made with MWCNTs polyscale coating on graphite felt and the cathode electrode with activated carbon coating on carbon cloth. In the MFC-MEC coupled system, the electrodes were connected in series using copper wire. The system was set up in a fed-batch mode and the landfill synthetic leachate was injected into the anode MFC-MEC chamber as fuel.

Results

In MFC, the highest voltage, current density and power density were 1114 mV, 44.2A/m³ and 49.24 W/m³, respectively. The maximum of the coulombic efficiency system was 94.10%. The highest removed COD, NH₄-N and P was 97.38%, 79.56% and 74.61%, respectively. In the MEC, the maximum of voltage input, current density and power density was 1106 mV, 43.88 A/m³and 48.54 W/m³, respectively. The maximum coulombic efficiency system was 125.54%. Also the highest removed COD, NH₄-N and P was 97.46%, 78.81% and 76.25%, respectively. The highest biogas production rate and its yield were 39 mL/L.d, and 0.0118 L/g CODrem, respectively.

Conclusion

This study found that the MFC-MEC coupled system had promising potential for strong wastewaters treatment, such as the leachate of landfill; and the in-site use of generated electricity and the production of useful fuels such as biogas.

Adsorption of methylene blue dye from aqueous solution by a novel PVA/CMC/halloysite nanoclay bio composite: Characterization, kinetics, isotherm and antibacterial properties

Here the fabrication of a novel PVA/CMC/halloysite nanoclay membrane for the effective adsorption of cationic dye (methylene blue, MB) from aqueous environment is reported. The membranes were analyzed through scanning electron microscopy (SEM), optical microscopy (OM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), contact angle and universal testing machine (UTM) analysis. The adsorption behavior of the membrane in terms of nanoclay loading, contact time, initial concentration of MB, pH and temperature were also discussed. The membrane exhibits excellent removal efficiency (99.5%) for MB in the optimal conditions such as nanoclay dose = 6 wt%, initial dye concentration = 10 ppm, contact time = 240 min, pH = 10 and temperature = 30 °C. Three isotherm models (Freundlich, Langmuir and Temkin) were employed to analyze the dye adsorption data. The results revealed that the adsorption process could be described well with both Freundlich and Langmuir isotherm model. The kinetics of MB adsorption onto membrane follows pseudo-second-order model while thermodynamic parameter indicate that adsorption is feasible and endothermic in nature. The antibacterial studies revealed that the PVA/CMC/halloysite nanoclay membrane possess notable antibacterial property. Finally, the desorption studies showed that the membrane have good reusability even after four recycles.

Green synthesis of SnO2-ZnO-eggshell nanocomposites and study of their application in removal of mercury (II) ions from aqueous solution

BACKGROUND

Mercury (Hg) in dental amalgam is the world's hidden source of mercury contamination. The development of more eco-friendly and cost-effective adsorbents to reduce mercury pollutants in wastewater is highly desirable and is still a major challenge. In this study, a novel nanocomposite was synthesized and used as an efficient adsorbent for the removal of Hg(II) ions from aqueous solution.

METHODS

A green and cost-effective method was described to the synthesis of SnO₂-ZnO-eggshell nanocomposites using teucrium polium extract as a renewable reductant and mild stabilizer. The biosynthesized nanocomposites were characterized by various techniques. The novel SnO₂-ZnO-eggshell nanocomposites were used as an effective adsorbent in the removal of mercury (II) ions. To achieve the maximum absorption efficiency of Hg(II) ions, the effect of operating factors such as pH value, the dose of catalyst, the initial metal concentration of Hg(II) ions, and catalyst type were evaluated.

RESULTS

The removal percentage and adsorption capacity of Hg(II) were obtained 99.15% and 396.6 mg.g^-1, respectively, under optimal conditions after 5 minutes. The selectivity of SnO₂-ZnO-eggshell nanocomposites for the adsorption of metal ions was studied, and the highest selectivity was obtained for adsorption of Hg (II) ions. Furthermore, the SnO₂- ZnO-eggshell nanocomposites could be recovered and reused at least three times without considerable loss of their efficiency.

CONCLUSIONS

The present approach has advantages such as rapidity, simplicity, selectivity, low cost and, most importantly, the use of nanocomposites containing a bio-waste material of eggshell for removal of Hg(II) ions from aqueous solution.