Photocatalytic treatment of tetracycline antibiotic wastewater by silver/TiO2 nanosheets/reduced graphene oxide and artificial neural network modeling

Advances in photocatalytic degradation of tetracycline using graphene-based composites in water: a systematic review and future directions

In this study, a comprehensive systematic review was conducted to better recognize the applicability of graphene-based photocatalytic processes for the degradation of tetracycline (TC) from water. A broad search strategy was developed for English language articles available in PubMed, Scopus, and Web of Science. The effect of parameters such as pH, TC concentration, photocatalyst dose, radiation source intensity, and the effect of graphene on the process, kinetics, and reuse of the photocatalyst were investigated. A total of 63 out of a possible 3498 retrieved records met inclusion criteria. The results showed that most related studies have increased since 2019. About 46.7% of the articles showed 90-100% TC removal efficiency and 59.52% of the studies had optimal pH equal to 5 and 6. Also, the widespread use of visible light had a significant trend. The effect of the dose of graphene in the catalyst was one of the most important and effective factors on the process; hence, the difference in efficiency with and without graphene was completely evident. This review indicated that the presence of graphene has been able to have a positive effect on increasing the efficiency of oxidation processes, and it can be used for environmental pollutants remediation.

Pharmaceutical wastewater treatment using TiO2 nanosheets deposited by cobalt co-catalyst as hybrid photocatalysts: combined experimental study and artificial intelligence modeling

In this work, we developed a facile method for photocatalytic deposition of cobalt atoms as co-catalyst on TiO2 nanosheets (Co(x)/T) under visible light instead of UV irradiation for the first time. The deposition of cobalt atoms on TNs in the Co(x)/T samples was confirmed by DRS, Raman spectroscopy, photoluminescence, nitrogen physisorption, and TEM analyses. The size of cobalt nanoparticles/cluster dispersed on the TiO2 nanosheets were in the range of 5–20 nm according to TEM results. The PXRD patterns showed that the crystal structure and the anatase phase of TNs were preserved in the Co(x)/T samples after the visible light-assisted deposition process. The Co(x)/T samples showed higher activity compared to pure TiO2 nanosheets for the visible light degradation of tetracycline (TC) as pharmaceutical pollutant due to presence of cobalt co-catalyst. We studied the effect of several parameters on the degradation process and proposed the mechanism of degradation according to quenching experiments results. Due to time-consuming and costly of experimental works, we designed two strong artificial intelligence (AI) models (artificial neural networks (ANN) and neuro-fuzzy inference systems (ANFIS)) to estimate the removal process of TC, and predict the removal percent of TC for new values of inputs before performing experiment. The experimental and computational studies showed that the fabricated photocatalysts are as promising candidates for industrial wastewater treatment to meet environmental regulations and provide a new avenue for practical implications.

Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches

Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R 2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.

Preparation, Characterization of Graphitic Carbon Nitride Photo-Catalytic Nanocomposites and Their Application in Wastewater Remediation: A Review

Energy crisis and environmental pollution are the major problems of human survival and development. Photocatalytic technology can effectively use solar energy and is prospective to solve the above-mentioned problems. Carbon nitride is a two-dimensional polymer material with a graphite-like structure. It has good physical and chemical stabilities, unique chemical and electronic energy band structures, and is widely used in the field of photocatalysis. Graphitic carbon nitride has a conjugated large π bond structure, which is easier to be modified with other compounds. thereby the surface area and visible light absorption range of carbon nitride-based photocatalytic composites can be insignificantly increased, and interface electron transmission and corresponding photogenerated carriers separation of streams are simultaneously promoted. Therefore, the present study systematically introduced the basic catalytic principles, preparation and modification methods, characterization and calculation simulation of carbon nitride-based photocatalytic composite materials, and their application in wastewater treatment. We also summarized their application in wastewater treatment with the aid of artificial intelligence tools. This review summarized the frontier technology and future development prospects of graphite phase carbon nitride photocatalytic composites, which provide a theoretical reference for wastewater purification.

Artificial neural network modeling of cefixime photodegradation by synthesized CoBi2O4 nanoparticles

CoBi₂O₄ (CBO) nanoparticles were synthesized by sol-gel method using polyvinylpyrrolidone (PVP) as a complexing reagent. For a single phase with the spinel structure, the formed gel was dried and calcined at four temperatures stages. Various methods were used to identify and characterize the obtained spinel, such as X-ray diffraction (XRD), scanning electron micrograph (SEM-EDX), transmission electron microscope (TEM), Fourier transform infrared (FT-IR), X-ray fluorescence (XRF), Raman, and UV-Vis spectroscopies. The photocatalytic activity of CBO was examined for the degradation of a pharmaceutical product cefixime (CFX). Furthermore, for the prediction of the CFX degradation rate, an artificial neural network model was used. The network was trained using the experimental data obtained at different pH with different CBO doses and initial CFX concentrations. To optimize the network, various algorithms and transfer functions for the hidden layer were tested. By calculating the mean square error (MSE), 13 neurons were found to be the optimal number of neurons and produced the highest coefficient of correlation R² of 99.6%. The relative significance of the input variables was calculated, and the most impacting input was proved to be the initial CFX concentration. The effects of some scavenging agents were also studied. The results confirmed the dominant role of hydroxyl radical OH^• in the degradation process. With the novel CoBi₂O₄/ZnO hetero-system, the photocatalytic performance has been enhanced, giving an 80% degradation yield of CFX (10 mg/L) at neutral pH in only 3 h.