Removing hazardous organic pollutants, such as 4-nitrophenol (4-NP) and Congo red (CR) dyes from aqueous media and CO2 from the atmospheric medium remains a significant challenge. Herein, we report a facile in-situ synthetic approach for fabricating CuO-ZnO heterostructure photocatalysts through the surfactant-assisted co-precipitation method. The catalytic results demonstrate that the Cu1O-ZnO photocatalyst exhibits excellent activity under direct sunlight irradiation, owing to the heterostructure formation between the CuO and ZnO. The Cu1O-ZnO photocatalyst showed higher reaction rate constant (k) values of 0.20 min−1 for 4-NP and 0.09 min−1 for CR compared to previous reports. Additionally, efficient CO2 reduction was also achieved over Cu1O-ZnO photocatalyst. The optical and structural characterization results indicate that the improved photocatalytic reduction and degradation observed for the Cu1O-ZnO photocatalyst can be attributed to the strong synergistic interaction between p-type CuO and n-type ZnO and the construction of the p-n heterojunction. As a result, the absorption of visible light distinctly increased and inhibited the recombination rate of the photo-created electron-hole (e−/h+). Furthermore, the Cu1O-ZnO photocatalyst exhibited remarkable durability and recyclability, retaining high photoactivity (≥ 93%) after five cycles, demonstrating its potential for real-world applications in the photocatalytic reduction and degradation reactions under direct sunlight irradiation.
In the present work, the pollutants of the municipal wastewater are reduced using Chlorella vulgaris microalgae. The pollutants that were treated are: Total organic carbon (TOC), Chemical oxygen demand (COD), Nitrate (NO3), and Phosphate (PO4). Firstly, the treatment was achieved at atmospheric conditions (Temperature = 25oC), pH 7 with time (1 – 48 h). To study the effect of other microorganisms on the reduction of pollutants, sterilized wastewater and unsterilized wastewater were used for two types of packing (cylindrical plastic and cubic polystyrene) as well as algae's broth (without packing), where the microalgae are grown on the packing then transported to the wastewater for treatment. Th
... Show MoreIn context of this paper we prepare high purity powder ZnO nanostructures by chemical method at low temperature solution and study the effect off annealing at high temperature, ZnO nanoparticles have been successfully synthesized by chemical method at 0Cᵒ solution. In this method, suddenly reaction is occurred between zinc acetate solution and sodium hydroxide solution at 0Cᵒ, annealing temperature of powder product surfactant plays an important role in morphological changes. The nanostructures have been characterized by X-ray diffraction (XRD), Scanning Electron Microscope (SEM), differential scanning calorimetry(DSC) and UV-visible .analysis Effect of annealing temperatures on the morphology , structure and optical properties is di
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In this work, pure Polypyrrole (PPy) and Polypyrrole (PPy)/Graphene (GN) was synthesized by in-situ polymerization in different weight percentages (0.1, 0.3, 0.5, 1, 3 and 5 wt.% (g)) of GN nano particles using chemical oxidation method at room temperature. The FTIR, SEM and electrical properties were studies for the nano composites. The result show that when concentration of GN Nano particle increase, the electrical conductivity increased and the graphene sheets were merging to form a continuous area of the GN through the polypyrrole base material. The FTIR spectra shows that the characteristics absorption peaks of polypyrrole that is, 1546.80, 1463.87 and 3400.27 cm-1(stretching vibration in the pyrrol
... Show MoreAdvanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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