Photocatalytic degradation of antibiotics by molecular assembly porous carbon nitride: Activity studies and artificial neural networks modeling

A comprehensive review on TiO2-based heterogeneous photocatalytic technologies for emerging pollutants removal from water and wastewater: From engineering aspects to modeling approaches

The increasing presence of emerging pollutants (EPs) in water poses significant environmental and health risks, necessitating effective treatment solutions. Originating from industrial, agricultural, and domestic sources, these contaminants threaten ecological and public health, underscoring the urgent need for innovative and efficient treatment methods. TiO2-based semiconductor photocatalysts have emerged as a promising approach for the degradation of EPs, leveraging their unique band structures and heterojunction schemes. However, few studies have examined the synergistic effects of operating conditions on these contaminants, representing a key knowledge gap in the field. This review addresses this gap by exploring recent trends in TiO2-driven heterogeneous photocatalysis for water and wastewater treatment, with an emphasis on photoreactor setups and configurations. Challenges in scaling up these photoreactors are also discussed. Furthermore, Machine Learning (ML) models play a crucial role in developing predictive frameworks for complex processes, highlighting intricate temporal dynamics essential for understanding EPs behavior. This capability integrates seamlessly with Computational Fluid Dynamics (CFD) modeling, which is also addressed in this review. Together, these approaches illustrate how CFD can simulate the degradation of EPs by effectively coupling chemical kinetics, radiative transfer, and hydrodynamics in both suspended and immobilized photocatalysts. By elucidating the synergy between ML and CFD models, this study offers new insights into overcoming traditional limitations in photocatalytic process design and optimizing operating conditions. Finally, this review presents recommendations for future directions and insights on optimizing and modeling photocatalytic processes.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

A review of advanced oxidation process towards organic pollutants and its potential application in fracturing flowback fluid

Fracturing flowback fluid (FFF) including various kinds of organic pollutants that do harms to people and new treatments are urgently needed. Advanced oxidation processes (AOPs) are suitable methods in consideration with molecular weight, removal cost and efficiency. Here, we summarize the recent studies about AOP treatments towards organic pollutants and discuss the application prospects in treatment of FFF. Immobilization and loading methods of catalysts, evaluation method of degradation of FFF, and continuous treatment process flow are discussed in this review. In conclusion, further studies are urgently needed in aspects of catalyst loading methods, macromolecule organic evaluation methods, industrial process, and pathways of macromolecule organics' decomposition.

Efficient removal of antibiotics from water resources is a public health priority: a critical assessment of the efficacy of some remediation strategies for antibiotics in water

This review discusses the fundamental principles and mechanism of antibiotic removal from water of some commonly applied treatment techniques including chlorination, ozonation, UV-irradiation, Fenton processes, photocatalysis, electrochemical-oxidation, plasma, biochar, anaerobicdigestion, activated carbon and nanomaterials. Some experimental shortfalls identified by researchers such as certain characteristics of degradation agent applied and the strategies explored to override the identified limitations are briefly discussed. Depending on interactions of a range of factors including the type of antibiotic compound, operational parameters applied such as pH, temperature and treatment time, among other factors, all reviewed techniques can eliminate or reduce the levels of antibiotic compounds in water to varying extents. Some of the reviewed techniques such as anaerobic digestion generally require longer treatment times (up to 360, 193 and 170 days, according to some studies), while others such as photocatalysis achieved degradation within short contact time (within a minimum of 30, but up to 60, 240, 300 and 1880 minutes, in some cases). For some treatment techniques such as ozonation and Fenton, it is apparent that subjecting compounds to longer treatment times may improve elimination efficiency, whereas for some other techniques such as nanotechnology, application of longer treatment time generally meant comparatively minimal elimination efficiency. Based on the findings of experimental studies summarized, it is apparent that operational parameters such as pH and treatment time, while critical, do not exert sole or primary influence on the elimination percentage(s) achieved. Elimination efficiency achieved rather seems to be due more to the force of a combination of several factors.

High-performance Fe-doped ZIF-8 adsorbent for capturing tetracycline from aqueous solution

Efficient removal of antibiotics from aqueous solution is of fundamental importance due to the increasingly severe antibiotic-related pollution. Herein, a high-performance Fe-ZIF-8-500 adsorbent was synthesized by Fe-doping strategy and subsequent activation with high-temperature. In order to evaluate the feasibility of Fe-ZIF-8-500 as an adsorbent for tetracycline (TC) removal, the adsorption properties of Fe-ZIF-8-500 were systematically explored. The results showed that the Fe-ZIF-8-500 exhibited ultrahigh adsorption capacity for TC with a record-high value of 867 mg g^-1. Additionally, the adsorption kinetics and isotherms for TC onto the Fe-ZIF-8-500 can be well-fitted by the pseudo-second-order kinetics model and the Freundlich model, respectively. The ultrahigh adsorption capacity of Fe-ZIF-8-500 can be explained by the synergistic effect of multi-affinities, i.e., surface complexation, electrostatic attraction, π-π interaction and hydrogen bonding. After being used for four cycles the adsorption capacity of Fe-ZIF-8-500 remains a high level, demonstrating its outstanding reusability. The ultrahigh adsorption capacity, excellent reusability, satisfactory water stability and easy-preparation nature of Fe-ZIF-8-500 highlight its bright prospect for removing tetracycline pollutant from wastewater.

Data mining for pesticide decontamination using heterogeneous photocatalytic processes

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides from the environment, the heterogeneous photocatalytic process is one of the most effective approaches. This study focuses on artificial intelligence (AI) techniques used to generate optimum predictive models for pesticide decontamination processes using heterogeneous photocatalytic processes. In the present study, 537 valid cases from 45 articles from January 2000 to April 2020 were filtered based on their content collected and analyzed. Based on cross-industry standard process (CRISP) methodology, a set of four classifiers were applied: Decision Trees (DT), Bayesian Network (BN), Support Vector Machines (SVM), and Feed Forward Multilayer Perceptron Neural Networks (MLP). To compare the accuracy of the selected algorithms, accuracy, and sensitivity criteria were applied. After the final analysis, the DT classification algorithm with seven factors of prediction, the accuracy of 91.06%, and sensitivity of 80.32% was selected as the optimal predictor model.

Green sonochemical synthesis of BaDy2NiO5/Dy2O3 and BaDy2NiO5/NiO nanocomposites in the presence of core almond as a capping agent and their application as photocatalysts for the removal of organic dyes in water

The present work reports the sonochemical synthesis of DBNO NC (dysprosium nickelate nanocomposite) using metal nitrates and core almond as a capping agent. In addition, the effects of the power of ultrasound irradiation were investigated. The BaDy2NiO5/Dy2O3 and BaDy2NiO5/NiO nanocomposites were synthesized with sonication powers of 50 and 30 W, respectively. The agglomerated nanoparticles were obtained using different sonication powers, including 15, 30, and 50 W. The results showed that upon increasing the sonication power, the particle size decreased. After characterization, the optical, electrical, magnetic, and photocatalytic properties of the NC were studied. The nanocomposites showed an antiferromagnetic behavior. In this study, the photocatalytic degradations of two dyes, AR14 and AB92, were investigated in the presence of DBNO NC. Furthermore, the effects of the amount of photocatalyst, the concentration of the dye solution, the type of organic dye, and light irradiation on the photocatalytic activity of the nanocomposite were studied. The results showed that with an increasing amount of catalyst and decreasing concentration of dye, the photocatalytic activity of the nanocomposite was increased. This activity for the degradation of AR14 is higher than that of AB92. Both AR14 and AB92 dyes show higher photocatalytic degradation under UV irradiation than under Vis irradiation.

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.