Degradation of Fluoxetine using catalytic ozonation in aqueous media in the presence of nano-γ-alumina catalyst: Experimental, modeling and optimization study

Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling

Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.

Tailoring the aluminum nanocrystal surface oxide for all-aluminum-based antenna-reactor plasmonic photocatalysts

Aluminum nanocrystals (AlNCs) are of increasing interest as sustainable, earth-abundant nanoparticles for visible wavelength plasmonics and as versatile nanoantennas for energy-efficient plasmonic photocatalysis. Here, we show that annealing AlNCs under various gases and thermal conditions induces substantial, systematic changes in their surface oxide, modifying crystalline phase, surface morphology, density, and defect type and concentration. Tailoring the surface oxide properties enables AlNCs to function as all-aluminum-based antenna-reactor plasmonic photocatalysts, with the modified surface oxides providing varying reactivities and selectivities for several chemical reactions.

Preparation of Fe2(MoO4)3/graphene/Ti nanocomposite electrode for visible-light photoelectrocatalytic degradation of organic pollutants

The massive emission of organic pollutants, specially organic dyes into water poses a serious threat to the environment and human health. Photoelectrocatalysis (PEC) has been regarded as an efficient, promising and green technology for organic pollution degradation and mineralization. Herein, Fe₂(MoO₄)₃/graphene/Ti nanocomposite was synthesized and applied as a superior photoanode in a visible-light PEC process for degradation and mineralization of an organic pollutant. First, the Fe₂(MoO₄)₃ was synthesized by the microemulsion-mediated method. Then, Fe₂(MoO₄)₃ and graphene particles were simultaneously immobilized on a titanium plate by the electrodeposition technique. The prepared electrode was characterized by XRD, DRS, FTIR and FESEM analyses. The ability of the nanocomposite was investigated in the Reactive Orange 29 (RO29) pollutant degradation by the PEC. The Taguchi method was used for the visible-light PEC experiments design. The efficiency of RO29 degradation was enhanced with increasing bias potential, number of Fe₂(MoO₄)₃/graphene/Ti electrodes, visible-light power and Na₂SO₄ (electrolyte) concentration. The pH of the solution was the most influential variable in the visible-light PEC process. Furthermore, the performance of the visible-light PEC was compared with photolysis, sorption, visible-light photocatalysis and electrosorption processes. The obtained results confirm the synergistic effect of these processes on RO29 degradation by the visible-light PEC.

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development

Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 μg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R² of 99.98%, adding to an MSE of 1.86 × 10^-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R² of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.

State of Art and Perspectives in Catalytic Ozonation for Removal of Organic Pollutants in Water: Influence of Process and Operational Parameters

The number of organic pollutants detected in water and wastewater is continuously increasing thus causing additional concerns about their impact on public and environmental health. Therefore, catalytic processes have gained interest as they can produce radicals able to degrade recalcitrant micropollutants. Specifically, catalytic ozonation has received considerable attention due to its ability to achieve advanced treatment performances at reduced ozone doses. This study surveys and summarizes the application of catalytic ozonation in water and wastewater treatment, paying attention to both homogeneous and heterogeneous catalysts. This review integrates bibliometric analysis using VOS viewer with systematic paper reviews, to obtain detailed summary tables where process and operational parameters relevant to catalytic ozonation are reported. New insights emerging from heterogeneous and homogenous catalytic ozonation applied to water and wastewater treatment for the removal of organic pollutants in water have emerged and are discussed in this paper. Finally, the activities of a variety of heterogeneous catalysts have been assessed using their chemical–physical parameters such as point of zero charge (PZC), pKa, and pH, which can determine the effect of the catalysts (positive or negative) on catalytic ozonation processes.

Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant

This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (R.Br.) J.Sm. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (PSO). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R² were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The PSO algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m³ of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m² of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.

Electrochemical removal of fluoxetine via three mixed metal oxide anodes and carbonaceous cathodes from contaminated water

In this work, the fluoxetine (FLX) removal has been studied via the anodic oxidation (AO) process. Anode electrodes were Ti/RuO₂, Ti/RuO₂-IrO₂, and Ti/RuO₂-IrO₂-SnO₂, and cathode electrodes were graphite and carbon nanotubes (CNTs). The performances of electrodes were compared in terms of FLX removal efficiency. As a result, Ti/RuO₂-IrO₂-SnO₂ and CNTs were the optimal anode and cathode, respectively. The properties of the optimal electrodes were investigated using scanning electron microscopy, atomic force microscopy and X-ray diffraction spectroscopy. Cyclic voltammetry analysis was performed to study the electrochemical behavior of electrodes. The effect of current intensity (mA), initial pH, initial FLX concentration (mg/L) and process time (min) on the FLX removal efficiency was investigated and the response surface methodology was applied for the optimization of the AO process. The results showed that at current intensity, pH, initial FLX concentration and process time of 500 mA, 6, 25 mg/L and 160 min, maximum FLX removal efficiency was observed, which was 96.25%. Gas Chromatography-Mass Spectrometry (GC-MS), and total organic carbon (TOC) analysis was determined to evaluate the intermediates, and mineralization efficiency. The TOC removal efficiency was reached 81.51% after 6 h under optimal experimental conditions, indicating the successful removal of the FLX.

Clay, Zeolite and Oxide Minerals: Natural Catalytic Materials for the Ozonation of Organic Pollutants

Ozone has been successfully employed in water treatment due to its ability to oxidize a wide variety of refractory compounds. In order to increase the process efficiency and optimize its economy, the implementation of heterogeneous catalysts has been encouraged. In this context, the use of cheap and widely available natural materials is a promising option that would promote the utilization of ozone in a cost-effective water treatment process. This review describes the use of natural clays, zeolites and oxides as supports or active catalysts in the ozonation process, with emphasis on the structural characteristics and modifications performed in the raw natural materials; the catalytic oxidation mechanism; effect of the operating parameters and degradation efficiency outcomes. According to the information compiled, more research in realistic scenarios is needed (i.e., real wastewater matrix or continuous operation in pilot scale) in order to transfer this technology to the treatment of real wastewater streams.

Novel catalytic degradation of Diazinon with ozonation/mg-Al layered double hydroxides: optimization, modeling, and dispersive liquid-liquid microextraction

PURPOSE

In this study MgAl- layered double hydroxides (MgAl-LDH) nanoparticles were prepared by a simple and fast co-precipitation method and used as a catalyst in the ozonation process to degrade diazinon from aqueous solutions.

METHODS

The structure of the synthesized MgAl-LDH was investigated by X-ray diffraction pattern (XRD) and field emission scanning electron microscope-energy dispersive spectroscopy (FESEM-EDX). The response surface methodology (RSM) was used to investigate the effects of different parameters including of reaction time, initial diazinon concentration, pH, and LDH dose on the removal of diazinon by MgAl-LDH catalytic ozonation process. Central Composite Design (CCD) was employed for the optimization and modeling of the process. Dispersive liquid-liquid microextraction (DLLME) method was used to extract diazinon from aqueous samples. The GC-Mass analysis was performed to determine intermediate compounds during diazinon degradation reactions. To evaluate the process performance, TOC and COD removal were measured under optimum conditions.

RESULTS

The highest removal efficiency of 92% was observed in optimum conditions as follow; initial diazinon concentration: 120 mg/L, pH: 8.25, LDH dose: 750 mg/L, and reaction time: 70 min. The quadratic model was obtained with a good fit. The removal of COD and TOC were 80% and 74%, respectively.

CONCLUSION

This process can be suggested and used in the treatment of various industrial wastewaters.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-021-00687-w.

Enhanced effect of pyrite on the removal of metronidazole by zero valent iron

The abuse and improper disposal of antibiotics including metronidazole (MNZ) result in serious contamination in aquatic environments. In this study, pyrite, which was not reactive for MNZ removal, was simply mixed with zero valent iron (ZVI) to efficiently remove MNZ in anaerobic aqueous solutions. A dual ZVI/pyrite system consisting of ZVI (1.0 g/L) and pyrite (4.0 g/L) removed MNZ completely in 360 min within a broad pH₀ range (5.0-9.0), and it still maintained a high removal efficiency (~80%) even at a high pH₀ of 10.0. By contrast, single ZVI (1.0 g/L) showed much lower efficiency (4.8%-22.0%) within the same pH₀ range (5.0-10.0). On investigating the mechanism of MNZ removal, the cooperation between ZVI and pyrite enhanced the surface corrosion of ZVI and facilitated the redox cycle of Fe(III)/Fe(II) to generate more sorbed Fe(II), which was a dominant reactive species for MNZ removal. Pyrite also activated the ZVI surface to form FeS@Fe in situ, accelerating the electron transfer from Fe⁰ core to the surface-enriched MNZ, and stimulated the formation of green rust sulfate on the ZVI surface to further promote MNZ removal. LC-MS analysis confirmed ZVI/pyrite reductively transformed MNZ into readily biodegradable products by denitration and cleavage of hydroxyethyl.

Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA

As a key parameter in the adsorption process, removal rate is not available under most operating conditions due to the time and cost of experimental testing. To address this issue, evaluation of the efficiency of NH4+ removal from stormwater by coal-based granular activated carbon (CB-GAC), a novel approach, the response surface methodology (RSM), back-propagation artificial neural network (BP-ANN) coupled with genetic algorithm (GA), has been applied in this research. The sorption process was modeled based on Box-Behnben design (BBD) RSM method for independent variables: Contact time, initial concentration, temperature, and pH; suggesting a quadratic polynomial model with p-value < 0.001, R2 = 0.9762. The BP-ANN with a structure of 4-8-1 gave the best performance. Compared with the BBD-RSM model, the BP-ANN model indicated better prediction of the response with R2 = 0.9959. The weights derived from BP-ANN was further analyzed by Garson equation, and the results showed that the order of the variables’ effectiveness is as follow: Contact time (31.23%) > pH (24.68%) > temperature (22.93%) > initial concentration (21.16%). The process parameters were optimized via RSM optimization tools and GA. The results of validation experiments showed that the optimization results of GA-ANN are more accurate than BBD-RSM, with contact time = 899.41 min, initial concentration = 17.35 mg/L, temperature = 15 °C, pH = 6.98, NH4+ removal rate = 63.74%, and relative error = 0.87%. Furthermore, the CB-GAC has been characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET). The isotherm and kinetic studies of the adsorption process illustrated that adsorption of NH4+ onto CB-GAC corresponded Langmuir isotherm and pseudo-second-order kinetic models. The calculated maximum adsorption capacity was 0.2821 mg/g.

MOF-derived Co3O4-C@FeOOH as an efficient catalyst for catalytic ozonation of norfloxacin

Metal-organic frameworks (MOFs) ZIF-67-derived Co₃O₄-C@FeOOH composite was prepared, characterized and used as an efficinet catalyst for ozonation of norfloxacin (NOF). Results showed that ZIF-67-derived Co₃O₄-C composite maintained the polyhedral structure of ZIF-67. After modification, abundant amorphous FeOOH nanowire attached on the surface of Co₃O₄-C composite, resulting in Co₃O₄-C@FeOOH interwoven polyhedrons. Furthermore, the specific surface area of the formed composite was about 2.5 times that of Co₃O₄-C composite, which might provide more active sites for catalytic reaction. Compared with single ozonation system, the catalytic ozonation process (Co₃O₄-C@FeOOH/O₃) had better performance in NOF mineralization under the same operating conditions. Moreover, in the presence of Co₃O₄-C@FeOOH, faster O₃ decomposition and higher •OH concentration were observed, which could explain the significant enhancement of TOC removal. The co-existence of Fe and Co in various valence states in catalyst might improve the conversion of Co(III)/Co(II) and Fe(III)/Fe(II), which would increase the catalytic activity in catalytic ozonation process. Besides, several main intermediate products were detected and possible NOF degradation pathway was proposed.

Simplified waste-free process for synthesis of nanoporous compact alumina under technologically advantageous conditions

Precipitated ammonium aluminium carbonate hydroxide (NH₄Al(OH)₂CO₃) is a promising precursor for preparation of nanostructured Al₂O₃. However, the experimental conditions, such as the low concentration of Al³⁺ salt solution, high temperature and/or pressure, long reaction time, and excessive amount of the (NH₄)₂CO₃ precipitating agent, make this process expensive for large-scale production. Here, we report a simpler and cheaper route to prepare nanostructured alumina by partial neutralisation of a nearly saturated aqueous solution of Al(NO₃)₃ with (NH₄)₂CO₃ as a base at pH < 4. Synthesis in the acidic region led to formation of a polynuclear aluminium cluster (Al₁₃), which is an important “green” solution precursor for large-area preparation of Al₂O₃ thin films and nanoparticles. Control of the textural properties of the final alumina product during calcination of the prepared aluminium (oxy)hydroxide gel was accomplished by adding low-solubility aluminium acetate hydroxide (Al(OH)(CH₃COO)₂) as a seed to the Al(NO₃)₃ solution before neutralisation. The large Brunauer–Emmett–Teller specific surface area (376 m² g⁻¹) and narrow pore size distribution (2–20 nm) of the prepared compact alumina suggest that the chelating effect of the acetate ions affects the structures of the forming transition aluminas, and the evolved gases produced by decomposition of Al(OH)(CH₃COO)₂ and NH₄NO₃ as a by-product of the reaction during calcination prevent particle agglomeration. Other advantages of the proposed process are its versatility and the ability to obtain high purity materials without producing large amounts of by-products without the need for washing and energy saving by using a low processing temperature, and the possibility of recycling the generated CO₂ and NH₃ gases as the (NH₄)₂CO₃ reagent.

Flow chart of the proposed process for production of nanoporous alumina monoliths.

Mechanistic insights into the catalytic ozonation process using iron oxide-impregnated activated carbon

In the present study, radiolabelled formate was used as a probe compound in order to gain mechanistic insight into the catalytic ozonation process using a commercially available iron oxide-impregnated activated carbon catalyst. We simultaneously analysed the adsorptive and oxidative removal of formate in order to determine the contribution of the catalyst to adsorption and oxidant generation processes respectively. Our results show that the presence of the catalyst enhanced ozone decay as well as the rate and extent of formate oxidation at pH 3.0 compared to that observed in the presence of ozone alone as a result of oxidant generation via O₃-Fe oxide interaction. A reduction in rate and extent of formate oxidation on addition of t-butanol and Cl^- (known hydroxyl radical (^•OH) scavengers under acidic conditions) provides evidence that the oxidant generated during catalytic ozonation at pH 3.0 is ^•OH. Moreover, the oxidation of formate during catalytic ozonation mostly occurs at the solid-liquid interface and/or in bulk solution with adsorption playing no role in the overall oxidation process with this finding supported by the exceptionally high oxidation efficiency compared to the extent of adsorption observed when no O₃ was added. While catalytic ozonation was effective in formate oxidation at pH 3.0, the presence of the catalyst did not lead to an increase in either the rate or extent of formate oxidation at pH 7.3 and 8.5 suggesting that only protonated iron oxide surface sites generate strong oxidant(s) on interaction with O₃. Based on our understanding of the processes operating during the ozonation and catalytic ozonation processes, a mathematical model has been developed that adequately describes the experimental results obtained here. Overall, this study shows that systematic measurement of ozone decay, removal of the parent compound as well as formation of the oxidized products under well controlled conditions are required for unequivocal elucidation of the mechanism of catalytic ozonation.

Advanced Oxidation Process for Degradation of Carbamazepine from Aqueous Solution: Influence of Metal Modified Microporous, Mesoporous Catalysts on the Ozonation Process

Carbamazepine (CBZ), a widely used pharmaceutical compound, is one of the most detected drugs in surface waters. The purpose of this work was to identify an active and durable catalyst, which, in combination with an ozonation process, could be used to remove CBZ and its degradation products. It was found that the CBZ was completely transformed after ozonation within the first minutes of the treatment. However, the resulting degradation products, 1-(2-benzaldehyde)-4-hydro-(1H,3H)-quinazoline-2-one (BQM) and 1-(2-benzaldehyde)-(1H,3H)-quinazoline-2,4-dione (BQD), were more resistant during the ozonation process. The formation and degradation of these products were studied in more detail and a thorough catalytic screening was conducted to reveal the reaction kinetics of both the CBZ and its degradation products. The work was performed by non-catalytic ozonation and with six different heterogeneous catalysts (Pt-MCM-41-IS, Ru-MCM-41-IS, Pd-H-Y-12-EIM, Pt-H-Y-12-EIM, Pd-H-Beta-300-EIM and Cu-MCM-41-A-EIM) operating at two temperatures 20 °C and 50 °C. The influence of temperature on degradation kinetics of CBZ, BQM and BQD was studied. The results exhibited a notable difference in the catalytic behavior by varying temperature. The higher reactor temperature (50 °C) showed a higher activity of the catalysts but a lower concentration of dissolved ozone. Most of the catalysts exhibited higher removal rate for BQM and BQD compared to non-catalytic experiments in both temperatures. The Pd-H-Y-12-EIM catalyst illustrated a higher degradation rate of by-products at 50 °C compared to other catalysts.

Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm

This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole acetic acid (IAA) and pseudomonas aeruginosa bacteria on increasing pyrene removal efficiency by phytoremediation process was studied. The experimental design was done using the Box-Behnken Design (BBD) technique. In the RSM model, the non-linear second-order model was in good agreement with the laboratory results. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one. Existence of eight neurons in the hidden layer leads to the highest R and lowest MSE and MAE. The results of the GA determined the optimum performance conditions. The results showed that using indole acetic acid and pseudomonas bacteria increased the efficiency of the sorghum plant in removing pyrene from the soil. The comparison obviously indicated that the prediction capability of the ANN model was much better than that of the RSM model.

Performance analysis of ultrasound-assisted synthesized nano-hierarchical SAPO-34 catalyst in the methanol-to-lights-olefins process via artificial intelligence methods

The present study has focused on performance analysis of ultrasound-assisted synthesized nano-hierarchical silico-alumino-phosphate-34 (SAPO-34) catalyst during methanol-to-light-olefins (MTO) process. A classical method, i.e., multiple linear regression (MLR) and two intelligent methods, i.e., genetic programming (GP) and artificial neural networks (ANN) were used for modeling of the performance of nano-hierarchical SAPO-34 catalyst. We studied the influence of basic parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst such as crystallization time, ultrasonic irradiation time, ultrasonic intensity, amount of organic template (diethylamine (DEA) and carbon nanotube (CNT)) on its performance (methanol conversion and light olefins selectivity) in MTO process. The results revealed that the models achieved using the GP method had the highest accuracy for training and test data. Therefore, GP models were used in the following to predict the effect of main parameters for the sonochemical synthesis of nano-hierarchical SAPO-34 catalyst. Finally, an optimal catalyst with the highest yield into light olefins was predicted using the genetic optimization algorithm. The genetic models were employed as an evaluation function in the genetic algorithm (GA). A good agreement between the outputs of GP models for the optimal catalyst and experimental results were obtained. The optimal ultrasound-assisted synthesized nano-hierarchical SAPO-34 was accompanied by light olefins selectivity of 77% and methanol conversion of 94% from the onset of the process after 9 h.

Application of Heterogeneous Catalytic Ozonation for Refractory Organics in Wastewater

Catalytic ozonation is believed to belong to advanced oxidation processes (AOPs). Over the past decades, heterogeneous catalytic ozonation has received remarkable attention as an effective process for the degradation of refractory organics in wastewater, which can overcome some disadvantages of ozonation alone. Metal oxides, metals, and metal oxides supported on oxides, minerals modified with metals, and carbon materials are widely used as catalysts in heterogeneous catalytic ozonation processes due to their excellent catalytic ability. An understanding of the application can provide theoretical support for selecting suitable catalysts aimed at different kinds of wastewater to obtain higher pollutant removal efficiency. Therefore, the main objective of this review article is to provide a summary of the accomplishments concerning catalytic ozonation to point to the major directions for choosing the catalysts in catalytic ozonation in the future.