A multivariate modeling and experimental realization of photocatalytic system of engineered S-C3N4/ZnO hybrid for ciprofloxacin removal: Influencing factors and degradation pathways

A review on hazards and treatment methods of released antibiotics in hospitals wastewater during the COVID-19 pandemic

Drugs and related goods are widely used in order to promote public health and the quality of life. One of the most serious environmental challenges affecting public health is the ongoing presence of antibiotics in the effluents generated by pharmaceutical industries and hospitals. Antibiotics cannot be entirely removed from wastewater using the traditional wastewater treatment methods. Unmetabolized antibiotics generated by humans can be found in urban and livestock effluent. The antibiotic present in effluent contributes to issues with resistance to antibiotics and the creation of superbugs. Over the recent 2 years, the coronavirus disease 2019 pandemic has substantially boosted hospital waste volume. In this situation, a detailed literature review was conducted to highlight the harmful effects of untreated hospital waste and outline the best approaches to manage it. Approximately 50 to 70% of the emerging contaminants prevalent in the hospital wastewater can be removed using traditional treatment strategies. This paper emphasizes the numerous treatment approaches for effectively eliminating emerging contaminants and antibiotics from hospital wastewater and provides an overview of global hospital wastewater legislation and guidelines on hospital wastewater administration. Around 90% of ECs might be eliminated by biological or physical treatment techniques when used in conjunction with modern oxidation techniques. According to this research, hybrid methods are the best approach for removing antibiotics and ECs from hospital wastewater. The document outlines the many features of effective hospital waste management and might be helpful during and after the coronavirus disease 2019 outbreak, when waste creation on all hospitals throughout the globe has considerably increased.

From wastewater to clean water: Recent advances on the removal of metronidazole, ciprofloxacin, and sulfamethoxazole antibiotics from water through adsorption and advanced oxidation processes (AOPs)

Antibiotics released into water sources pose significant risks to both human health and the environment. This comprehensive review meticulously examines the ecotoxicological impacts of three prevalent antibiotics-ciprofloxacin, metronidazole, and sulfamethoxazole-on the ecosystems. Within this framework, our primary focus revolves around the key remediation technologies: adsorption and advanced oxidation processes (AOPs). In this context, an array of adsorbents is explored, spanning diverse classes such as biomass-derived biosorbents, graphene-based adsorbents, MXene-based adsorbents, silica gels, carbon nanotubes, carbon-based adsorbents, metal-organic frameworks (MOFs), carbon nanofibers, biochar, metal oxides, and nanocomposites. On the flip side, the review meticulously examines the main AOPs widely employed in water treatment. This includes a thorough analysis of ozonation (O3), the photo-Fenton process, UV/hydrogen peroxide (UV/H2O2), TiO2 photocatalysis, ozone/UV (O3/UV), radiation-induced AOPs, and sonolysis. Furthermore, the review provides in-depth insights into equilibrium isotherm and kinetic models as well as prospects and challenges inherent in these cutting-edge processes. By doing so, this review aims to empower readers with a profound understanding, enabling them to determine research gaps and pioneer innovative treatment methodologies for water contaminated with antibiotics.

A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants

Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). Imputation method was applied to deal with the missing data. A generative ML model Vanilla Gan was utilized to create synthetic data to further augment the size of available dataset and the SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results indicated that data imputation allowed for the full utilization of the limited dataset, leading to good machine learning prediction performance and preventing common overfitting problems with small-sized data. Additionally, augmenting experimental data with synthetic data significantly improved prediction accuracy and considerably reduced overfitting issues. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

ZnO-Ti3C2TX composites supported on polyacrylic acid/chitosan hydrogels as high-efficiency and recyclable photocatalysts for norfloxacin degradation

Photocatalysts immobilized on hydrogels is a win-win mode, which not only improves photocatalysis but also successfully prevents catalyst loss, making it easy to separate and reuse during catalytic process. Here, ZnO-Ti3C2TX photocatalysts are loaded into the chitosan/polyacrylic acid hydrogel networks, realizing the efficiently photocatalytic degradation of norfloxacin. The chitosan-based composite hydrogel features rich functional groups and a dense pore structure, which is beneficial to antibiotic enrichment and photocatalytic degradation. The effects of different catalyst ratios, dosage, initial concentrations and pH on the degradation efficiency are investigated. The norfloxacin degradation rate constant is 0.012 min-1 and its degradation efficiency reaches up to 90 % after 240 min. Importantly, the photocatalytic composite hydrogel still retains 85 % degradation efficiency after 6 cycles. Moreover, e- plays a significant role in the degradation process. This work converts the traditional powder photocatalysts into bulk photocatalysts (photocatalytic hydrogels) to accomplish efficient degradation and rapid recycling for contaminant removal.

Hydrothermal fabrication, characterization and RSM optimization of cobalt-doped zinc oxide nanoparticles for antibiotic photodegradation under visible light

Photodegradation is considered a significant method engaged for the elimination of organic pollutants from water. In this work, hydrothermal cobalt-doped zinc oxide nanoparticles (Hy-Co-ZnO NPs) loaded with 5, 10, and 15% cobalt were prepared in a hydrothermal way and were investigated as a photocatalyst for the Ciprofloxacin (CIPF) degradation under visible irradiation using LED-light. Characterization approaches such as FTIR, XRD, XPS, DRS UV-vis spectroscopy, SEM, TEM, BET, EDX and TGA were used for the investigation of the fabricated Hy-Co-ZnO NPs. The studies indicated that 10% Hy-Co-ZnO NPs was the most efficient catalyst for the CIPF photolysis compared to ZnO NPs and other Hy-Co-ZnO NPs with 5 and 15% cobalt content. Higher photocatalytic activity (> 98%) of 20 mg/L of CIPF solution was attained within 60 min. The reaction kinetics showed that the first-order model is suitable for displaying the rate of reaction and amount of CIPF elimination with R2 = 0.9883. Moreover, Central composite design (CCD) optimization of the 10% Hy-Co-ZnO NPs was also studied.

Visible Light-Assisted Degradation of Sulfamethoxazole on 2D/0D Sulfur-Doped Bi2O3/MnO2 Z-Scheme Heterojunction Immobilized Photocatalysts

Retrieving the spent photocatalysts from the reaction system is always a challenging task. Therefore, the present work is focused on immobilizing sulfur-doped-Bi2O3/MnO2 (S-BOMO) heterojunction photocatalysts over different support matrices and evaluating their performance for the removal of sulfamethoxazole (SMX) in water under visible light. Our findings revealed S-BOMO coated clay beads (S-BOMO CCB) achieving more than 86% (240 min) SMX degradation ∼3, ∼1.3, and ∼2 times higher compared to S-BOMO coated on the different substrates, including glass beads, floating stones, and polymer material substrates, respectively. Mott-Schottky measurements confirmed the construction of the Z-scheme heterojunction involving MnO2 and 2S-Bi2O3. This Z-scheme mechanism, along with its narrow band gap of 1.58 eV, resulted in a rapid spatial transfer of the photogenerated charge carriers between the semiconductors and is believed to enhance the overall photocatalytic activity of the nanocomposite. Radical trapping and electron paramagnetic resonance results clearly established the active role of hydroxyl radicals and hydrogen peroxide in the degradation of SMX. Further, the 2S-BOMO CCB demonstrated excellent stability and photocatalytic activity over multiple runs. According to the sensitivity analysis and the results of anion effect experiments, phosphate and sulfate ions exhibit a significant impact on sulfamethoxazole degradation. Toxicity analysis revealed that 2S-BOMO CCB and sulfamethoxazole degradation byproducts were apparently innocuous. Additionally, the practical applicability of 2S-BOMO CCB was examined in various real water matrices, with the degradation efficiency followed the order: tap water < groundwater < surface water < hospital wastewater < municipal wastewater < pharmaceutical industry wastewater. The economic assessment revealed the reduction in the overall cost of the immobilized 2S-BOMO following the recovery process. Overall, the findings of this work provided critical insights into the synthesis and performance of incredibly effective and stable immobilized photocatalysts for the degradation of pharmaceutical pollutants.

Cube-shaped Cobalt-doped zinc oxide nanoparticles with increased visible-light-driven photocatalytic activity achieved by green co-precipitation synthesis

From the perspective of environmental protection, the highly efficient degradation of antibiotics and organic dyes in wastewater needs to be tackled as soon as possible. In this study, an ecofriendly and green cube-shaped cobalt-doped zinc oxide nanoparticles (Co-ZnO NPs) photocatalyst using Pterocladia Capillacea (P. Capillacea) water extract loaded with 5, 10, and 15% cobalt ions were formed via co-precipitation process to degrade antibiotics. The prepared Co-ZnO NPs were tested as a photocatalyst for the photodegradation of ciprofloxacin (CIPF) in the presence of a visible LED-light source. Co-ZnO NPs have been obtained through the co-precipitation method in the presence of P. Capillacea extract as a green capping agent and reducing agent, for the first time. Several characterization techniques including FTIR, XRD, BET, XPS, TEM, EDX, SEM, TGA and DRS UV-Vis spectroscopy were applied to study the prepared Co-ZnO NPs. XRD results suggested that the average size of these NPs ranged between 42.82 and 46.02 nm with a hexagonal wurtzite structure. Tauc plot shows that the optical energy bandgap of ZnO NPs (3.19 eV) gradually decreases to 2.92 eV by Co doping. Examinations showed that 5% Co-ZnO NPs was the highest efficient catalyst for the CIPF photodegradation when compared with ZnO NPs and other 10 and 15% Co-ZnO NPs. A 10 mg/L solution of CIPF was photo-degraded (100%) within the first 15 min irradiation. The kinetics showed that the first-order model is suitable for displaying the rate of reaction and amount of CIPF elimination with R2 = 0.952. Moreover, central composite design optimization of the 5% Co-doped ZnO NPs was also investigated.

Mn-Modified ZnO Nanoflakes for Optimal Photoelectrochemical Performance Under Visible Light: Experimental Design and Theoretical Rationalization

Doping of zinc oxide (ZnO) with manganese (Mn) tunes midbandgap states of ZnO to enhance its optical properties and makes it into an efficient photoactive material for photoelectrochemical water splitting, waste removal from water, and other applications. We demonstrate that ZnO modified with 1 at. % Mn exhibits the best performance, as rationalized by experimental, structural, and optical characterization and theoretical analysis. ZnO doped with the optimal Mn content possesses improved light absorption in the visible region and minimizes charge carrier recombination. The doping is substitutional and creates midgap states near the valence band. Mn atoms break localized charge traps at oxygen vacancy sites and eliminate photoluminescence peaks associated with oxygen vacancies. The optimal performance of Mn-modified ZnO is demonstrated with the photodegradation of Congo red and photoelectrochemical water splitting.

Recent advances in g-C3N4-based photocatalysis for water treatment: Magnetic and floating photocatalysts, and applications of machine-learning techniques

Over the past decade, there has been a substantial increase in research investigating the potential of graphitic carbon nitride (g-C3N4) for various environmental remediations. Renowned for its photocatalytic activity under visible light, g-C3N4 offers a promising solution for treating water pollutants. However, traditional g-C3N4-based photocatalysts have inherent drawbacks, creating a disparity between laboratory efficacy and real-world applications. A primary practical challenge is their fine-powdered form, which hinders separation and recycling processes. A promising approach to address these challenges involves integrating magnetic or floating materials into conventional photocatalysts, a strategy gaining traction within the g-C3N4-based photocatalyst arena. Another emerging solution to enhance practical applications entails merging experimental results with contemporary computational methods. This synergy seeks to optimize the synthesis of more efficient photocatalysts and pinpoint optimal conditions for pollutant removal. While numerous review articles discuss the laboratory-based photocatalytic applications of g-C3N4-based materials, there is a conspicuous absence of comprehensive coverage regarding state-of-the-art research on improved g-C3N4-based photocatalysts for practical applications. This review fills this void, spotlighting three pivotal domains: magnetic g-C3N4 photocatalysts, floating g-C3N4 photocatalysts, and the application of machine learning to g-C3N4 photocatalysis. Accompanied by a thorough analysis, this review also provides perspectives on future directions to enhance the efficacy of g-C3N4-based photocatalysts in water purification.

Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green

This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO₃ composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light intensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail.

Green synthesis of two-electron centre based ZnO/NiCo2S4 QDs-OVs using Punica granatum fruit peel extract for an exceptional visible light photocatalytic degradation of doxycycline and ciprofloxacin

Biosynthesis of nanomaterials using plant extract makes them attractive in the field of photocatalysis as they are environmental friendly. The current study focused on the biosynthesis of ZnO/NiCo₂S₄ QDs (NCs) using Punica granatum fruit peel extract as the reducing agent. The nanomaterials were characterized with XRD, FTIR, Raman, SEM, TEM, UV-vis DRS, BET, PL, EIS, and ESR analysis and were used for photocatalytic degradation of doxycycline (DOX) and ciprofloxacin (CIP). The bandgap of ZnO is 3.2 eV, and the decoration of NiCo₂S₄ QDs aids in narrowing the bandgap (2.8 eV), making the NCs visible light active. The fabricated NCs achieved 99 and 89% degradation of DOX and CIP respectively. The photocatalytic efficiency of ZnO/NiCo₂S₄ QDs was much higher compared to individual ZnO and NiCo₂S₄ QDs. The half-life period of DOX and CIP were evaluated to be 58 and 152 min respectively. The percentage of TOC removal in the photodegraded product of DOX and CIP was estimated to be 99 and 89% respectively, indicating the mineralization of the compounds. The enhanced photocatalytic efficiency of the NCs was attributed to the narrowed visible light active bandgap, synergistic charge transfer across the interface, and lower charge recombination. The intermediates formed during the photocatalytic degradation of DOX and CIP were analyzed using GC-MS/MS analysis, and the photodegradation pathway was elucidated. Also, the toxicity of the intermediates was computationally analyzed using ECOSAR software. The fabricated ZnO/NiCo₂S₄ QDs have excellent stability and reusability, confirmed by XRD and XPS analysis. The reusable efficiency of the NCs for the photocatalytic degradation of DOX and CIP were 98.93, and 99.4% respectively. Thus, the biologically fabricated NCs are shown to be an excellent photocatalyst and have wide applications in environmental remediation.

Electrospun Porous Nanofibers: Pore−Forming Mechanisms and Applications for Photocatalytic Degradation of Organic Pollutants in Wastewater

Electrospun porous nanofibers have large specific surface areas and abundant active centers, which can effectively improve the properties of nanofibers. In the field of photocatalysis, electrospun porous nanofibers can increase the contact area of loaded photocatalytic particles with light, shorten the electron transfer path, and improve photocatalytic activity. In this paper, the main pore−forming mechanisms of electrospun porous nanofiber are summarized as breath figures, phase separation (vapor−induced phase separation, non−solvent−induced phase separation, and thermally induced phase separation) and post−processing (selective removal). Then, the application of electrospun porous nanofiber loading photocatalytic particles in the degradation of pollutants (such as organic, inorganic, and bacteria) in water is introduced, and its future development prospected. Although porous structures are beneficial in improving the photocatalytic performance of nanofibers, they reduce their mechanical properties. Therefore, strategies for improving the mechanical properties of electrospun porous nanofibers are also briefly discussed.

Efficient photocatalytic degradation of emerging ciprofloxacin under visible light irradiation using BiOBr/carbon quantum dot/saponite composite

The use of visible-driven photocatalysts has fascinated attention as a capable and sustainable approach for wastewater remediation. In this work, BiOBr/carbon quantum dot (CQDs)/saponite composites (CQDs/Clay@BiOBr) were fabricated via hydrothermally using two different CQDs/Clay precursors (in-situ synthesis (IS) and physical mixing (PM)). The obtained products were characterized, and the photocatalytic performances of the prepared samples were evaluated in the photocatalytic decomposition of emerging ciprofloxacin (CIP) pharmaceutical waste. The highest CIP mineralization performance was achieved when a combination of BiOBr and CQDs/Clay (IS) with the appropriate proportion because the strong adhesion between CQDs and clay generate a great heterojunction in the composite. The stronger interaction of CQDs and better distribution of CQDs on the surface of clay in the CQDs/Clay (IS) enhanced the interaction of BiOBr and CQDs, and avoided the re-agglomeration of excess of CQDs on surface of BiOBr which reduce the active surface to receive the light and react with CIP. The ultrafast degradation rate of the optimized CQDs/Clay@BiOBr composite was better compared to others. The significant improvement in the CIP degradation efficiency of the CQDs/Clay@BiOBr composite was attributed to the excellent separation and transportation of photogenerated electrons and holes, as confirmed by photoluminescence, photocurrent density, and electrochemical impedance spectroscopy results. Moreover, the photocatalytic degradation mechanism of CIP in the CQDs/Clay@BiOBr composite was proposed based on the electronic states of each material in the composite and on a scavenger test. Thus, the proposed CQDs/Clay@BiOBr composite can be employed as a potential visible-light-driven photocatalyst for the decomposition of organic contaminants in wastewater.

Nanoengineered metal-organic framework for adsorptive and photocatalytic mitigation of pharmaceuticals and pesticide from wastewater

Rapidly expanding water pollution has transformed into significant dangers around the world. In recent years, the pharmaceutical and agriculture field attained enormous progress to meet the necessities of health and life; however, discharge of trace amounts of pharmaceuticals and pesticides into water significantly have a negative influence on human health and the environment. Contamination with these pollutants also constitutes a great threat to the aquatic ecosystem. To deal with the harmful impacts of such pollutants, their expulsion has attracted researchers' interest a lot, and it became essential to figure out techniques suitable for the removal of these pollutants. Thus, many researchers have devoted their efforts to improving the existing technology or providing an alternative strategy to solve this environmental problem. One of the attractive materials for this purpose is metal-organic frameworks (MOFs) due to their superior high surface area, high porosity, and the tunable features of their structures and function. Among various techniques of wastewater treatment, such as biological treatment, advanced oxidation process and membrane technologies, etc., metal-organic frameworks (MOFs) materials are tailorable porous architectures and are viably used as adsorbents or photocatalysts for wastewater treatment due to their porosity, tunable internal structure, and large surface area. MOFs are synthesized by various methods such as solvo/hydrothermal, sonochemical, microwave and mechanochemical methods. Most common method used for the synthesis of MOFs is solvothermal/hydrothermal methods. Herein, this review aims at providing a comprehensive overview of the latest advances in MOFs and their derivatives, focusing on the following aspects: synthesis and applications. This review comprehensively highlights the application of MOFs and nano-MOFs to remove pharmaceuticals and pesticides from wastewater. For the past years, transition metal-based MOFs have been concentrated as photocatalyst/adsorbents in treating contaminated water. However, work on main group metal-based MOFs is not so abundant. Hence, the foremost objective of this review is to present the latest material and references concerning main group element-based MOFs and nanoscale materials derived from them towards wastewater treatment. It summarizes the possible research challenges and directions for MOFs and their derivatives as catalysts applied to wastewater treatment in the future. With the context of recent pioneering studies on main group elements-based MOFs and their derivatives; we hope to stimulate some possibilities for further development, challenges and future perspectives in this field have been highlighted.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

An assessment of hospital wastewater and biomedical waste generation, existing legislations, risk assessment, treatment processes, and scenario during COVID-19

Hospitals release significant quantities of wastewater (HWW) and biomedical waste (BMW), which hosts a wide range of contaminants that can adversely affect the environment if left untreated. The COVID-19 outbreak has further increased hospital waste generation over the past two years. In this context, a thorough literature study was carried out to reveal the negative implications of untreated hospital waste and delineate the proper ways to handle them. Conventional treatment methods can remove only 50%-70% of the emerging contaminants (ECs) present in the HWW. Still, many countries have not implemented suitable treatment methods to treat the HWW in-situ. This review presents an overview of worldwide HWW generation, regulations, and guidelines on HWW management and highlights the various treatment techniques for efficiently removing ECs from HWW. When combined with advanced oxidation processes, biological or physical treatment processes could remove around 90% of ECs. Analgesics were found to be more easily removed than antibiotics, β-blockers, and X-ray contrast media. The different environmental implications of BMW have also been highlighted. Mishandling of BMW can spread infections, deadly diseases, and hazardous waste into the environment. Hence, the different steps associated with collection to final disposal of BMW have been delineated to minimize the associated health risks. The paper circumscribes the multiple aspects of efficient hospital waste management and may be instrumental during the COVID-19 pandemic when the waste generation from all hospitals worldwide has increased significantly.

Photocatalytic performance of 3D engineered chitosan hydrogels embedded with sulfur-doped C3N4/ZnO nanoparticles for Ciprofloxacin removal: Degradation and mechanistic pathways

Ciprofloxacin, a biotoxic micropollutant, is ubiquitously found in the water environment, which is a global concern. This study developed polymeric S-C3N4/ZnO-Chitosan (indexed as SCZ-CH) hydrogels for degrading Ciprofloxacin. The SCZ-CH hydrogels provided the Ciprofloxacin degradation efficiencies of ~93% and ~69% in UV and visible lights, respectively, at optimum conditions (SCZ-CH hydrogels with 2 g/L SCZ, 20 mg/L initial concentration, pH 5, and room temperature). In addition, immobilized SCZ-CH hydrogels structures enable easy separation of the SCZ catalyst from water. The spectroscopic and microscopic analyses of SCZ-CH hydrogels show multifaceted properties, like high oxygen concentrations, crystallinity, stacked structure, high roughness, and improved bandgap energy, which are responsible for the enhanced photocatalytic activity. The effects of water matrix and experimental conditions on Ciprofloxacin degradation were also studied, which suggested that the catalyst dose and solution pH have significant effects on photocatalytic activity. SCZ-CH hydrogels have shown good mineralization efficiency (~98%) and reusability (up to 10 cycles) for Ciprofloxacin removal. Superoxide radicals played an essential role in the degradation of Ciprofloxacin. The Ciprofloxacin molecules get degraded by driving radicals through oxidation, defluorination, substitution, and breaking of the rings. The proposed SCZ-CH hydrogels can be effectively used at a large scale to treat micropollutants.

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.

A review on hospital wastewater treatment: A special emphasis on occurrence and removal of pharmaceutically active compounds, resistant microorganisms, and SARS-CoV-2

The hospital wastewater imposes a potent threat to the security of human health concerning its high vulnerability towards the outbreak of several diseases. Furthermore, the outbreak of COVID-19 pandemic demanded a global attention towards monitoring viruses and other infectious pathogens in hospital wastewater and their removal. Apart from that, the presence of various recalcitrant organics, pharmaceutically active compounds (PhACs), etc. imparts a complex pollution load to water resources and ecosystem. In this review, an insight into the occurrence, persistence and removal of drug-resistant microorganisms and infectious viruses as well as other micro-pollutants have been documented. The performance of various pilot/full-scale studies have been evaluated in terms of removal of biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), PhACs, pathogens, etc. It was found that many biological processes, such as membrane bioreactor, activated sludge process, constructed wetlands, etc. provided more than 80% removal of BOD, COD, TSS, etc. However, the removal of several recalcitrant organic pollutants are less responsive to those processes and demands the application of tertiary treatments, such as adsorption, ozone treatment, UV treatment, etc. Antibiotic-resistant microorganisms, viruses were found to be persistent even after the treatment of hospital wastewater, and high dose of chlorination or UV treatment was required to inactivate them. This article circumscribes the various emerging technologies, which have been used to treat PhACs and pathogens. The present review also emphasized the global concern of the presence of SARS-CoV-2 RNA in hospital wastewater and its removal by the existing treatment facilities.