Modeling approach for Ti3C2 MXene-based fluorescent aptasensor for amoxicillin biosensing in water matrices.

Amoxicillin, a member of the penicillin family, is primarily utilized for the treatment of various bacterial infections affecting ears, nose, throat, urinary tract, and skin. Given its widespread application in medicine, agriculture, environment, and food industry, the precise and sensitive detection of amoxicillin is important. This study introduces a novel approach to developing a sensitive and selective fluorescent aptasensor relying on fluorescence resonance energy transfer (FRET) for the specific detection of amoxicillin. The carboxyfluorescein-labeled aptamer serves as a energy donor, while MXene functions as an energy acceptor, and acting as a quencher. To achieve optimal detection efficiency, a dual optimization strategy utilizing RSM-CCD and ANN-GA was used to fine-tune experimental conditions. The fluorescence measurements revealed an expansive linear range extending from 100 to 2400 ng mL-1, accompanied by an exceptionally low detection limit of 1.53 ng mL-1. Additionally, it shows an excellent selectivity towards amoxicillin over other antibiotics commonly found in water matrices. The aptasensor demonstrates good stability and reproducibility; effectiveness of the aptasensor was validated by testing in real water samples. This remarkable sensitivity and broad dynamic range affirm the efficacy aptasensor in accurately detecting varying concentrations of amoxicillin in wastewater bodies.

Harnessing artificial intelligence-driven approach for enhanced indole-3-acetic acid from the newly isolated Streptomyces rutgersensis AW08.

Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces-like strain isolated from Lake Oubeira sediments (El Kala, Algeria) for further investigations (i.e., 16S rRNA gene barcoding and process optimization). Subsequently, artificial intelligence-based approaches were employed to maximize IAA bioproduction on spent coffee grounds as high-value-added feedstock. The specificity was the novel application of the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Box (L-BFGS-B) optimization algorithm. The new strain AW08 was a significant producer of IAA (26.116 ± 0.61 µg/mL) and was identified as Streptomyces rutgersensis by 16S rRNA gene barcoding and phylogenetic inquiry. The empirical data involved the inoculation of AW08 in various cultural conditions according to a four-factor Box Behnken Design matrix (BBD) of Response surface methodology (RSM). The input parameters and regression equation extracted from the RSM-BBD were the basis for implementing and training the L-BFGS-B algorithm. Upon training the model, the optimal conditions suggested by the BBD and L-BFGS-B algorithm were, respectively, L-Trp (X1) = 0.58 %; 0.57 %; T° (X2) = 26.37 °C; 28.19 °C; pH (X3) = 7.75; 8.59; and carbon source (X4) = 30 %; 33.29 %, with the predicted response IAA (Y) = 152.8; 169.18 µg/mL). Our findings emphasize the potential of the multifunctional S. rutgersensis AW08, isolated and reported for the first time in Algeria, as a robust producer of IAA. Validation investigations using the bioprocess parameters provided by the L-BFGS-B and the BBD-RSM models demonstrate the effectiveness of AI-driven optimization in maximizing IAA output by 5.43-fold and 4.2-fold, respectively. This study constitutes the first paper reporting a novel interdisciplinary approach and providing insights into biotechnological advancements. These results support for the first time a reasonable approach for valorizing spent coffee grounds as feedstock for sustainable and economic IAA production from S. rutgersensis AW08.

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.

Synthesis of novel PANI/PVA-NiCu composite material for efficient removal of organic dyes.

The present work aims the synthesis of a novel, low cost, and environmentally friendly PANI/PVA-CuNi composite by chemical oxidative polymerization of aniline monomer and polyvinyl alcohol (PVA) as film matrix; several percentages of copper (Cu) and Nickel (Ni) were used. UV-Visible spectroscopy, FTIR, SEM-EDX, and TGA were used to characterize the nanocomposites. While PANI/PVA-CuNi nanocomposites were investigated in adsorption experiments of methylene blue (MB) under different controlled conditions (time reaction, adsorbent dosage, initial dye concentration, stirring speed, temperature, and pH of the medium) also various kinetic models were employed to evaluate the efficiency of the adsorption. The results revealed that the10 mg of PANI/PVA-Cu50Ni50 and PANI/PVA-Ni composites Catalyst removed (94% and 93% of methylene blue in 180 min respectively at 10-5 M initial concentration of dye, pH of 13, stirring speed of 150 rpm, the temperature of 301 k. the kinetics data were properly fitted with the pseudo second-order model with a correlation coefficient of 0.98262 and 0.95881 using PANI/PVA-Cu50Ni50 and PANI/PVA-Ni, respectively.

Latest eco-friendly approaches for pesticides decontamination using microorganisms and consortia microalgae: A comprehensive insights, challenges, and perspectives.

Pesticides are chemical compounds that are considered toxic to many organisms, including humans. Their elimination from polluted sites attracted the attention of Scientifics in the last decade; Among the various methods used to decontaminate pesticides from the environment, the microbial-algae consortium is a promising bioremediation technology, which implies several advantages as an eco-friendly process that generate biomass produced that could be valorized in the form of bioenergy, In this review, we will discuss the latest eco-friendly approaches using microorganisms to remediate sites contaminated by pesticides, and shows the ability of microbial, algae and their consortium to remove pesticides and the role of different enzymes in degradation processes.

Degradation of Azithromycin from aqueous solution using Chlorine-ferrous- oxidation: ANN-GA modeling and Daphnia magna biotoxicity test assessment.

Azithromycin (AZM), an antibacterial considered one of the most consumed drugs, especially during the period against the Covid 19 pandemic, and it is one of the persistent contaminants that can be released into aquatic ecosystems. The purpose of this study is to determine the efficacy of a Fenton-like process (chlorine/iron) for the degradation of AZM in an aqueous medium by determining the impact of several factors (the initial concentration of (FeSO4, NaClO, pollutant), and the initial pH) on the degradation rate. The Response Surface Methodology (RSM) based on the Box-Wilson design as well as the Artificial Neural Network (ANN) modeling combined with a genetic algorithm (GA) approaches were used to determine the optimal levels of the selected variables and the optimal rate of degradation. The quadratic model of multi-linear regression developed indicated that the optimal conditions were a concentration of chlorine of 600 µM, the concentration of AZM is 32.8 mg/L, the mass of the catalyst FeSO4 is 3.5 mg and a pH of 2.5, these optimal values gave a predicted and experimental yield of 64.05% and 70% respectively, the lack of fit test in RSM modeling (F0 = 3.31 which is inferior to Fcritic (0.05, 10.4) = 5.96) indicates that the true regression function is not linear therefore, the ANN-GA modeling as non-linear regression indicated that the optimal conditions were a concentration of chlorine of 256 µM, the concentration of AZM is 5 mg/L, the mass of the catalyst FeSO4 is 9.5 mg and a pH of 2.8, these optimal values gave a predicted and experimental yield of 79.69% and close to 80% respectively, Furthermore, biotoxicity tests were conducted to confirm the performance of our process using bio-indicators called daphnia (Daphnia magna), which demonstrated the efficacy of the like-Fenton process after 4 h of degradation.

Removal of the industrial azo dye crystal violet using a natural clay: Characterization, kinetic modeling, and RSM optimization.

Natural clay (NC) was employed as a natural adsorbent for the elimination of an azo dye Crystal Violet (CV) from aqueous media. The characterization of the clay was performed by X-ray diffractometry (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM) equipped with an X-ray energy dispersion spectrometer (EDS) and Brunauer-Emmett-Teller (BET) specific surface area analysis. The Box-Behnken design (BBD) was used as a powerful tool to determine the stationary points of the main independent factors: initial CV concentration, initial pH, temperature, and adsorbent dose on the adsorption efficiency. The significance and adequacy of the model were investigated using analysis of variance (ANOVA). The obtained results indicated an optimal dye removal of 99.1% at pH = 3, initial CV concentration of 11.767 mg/L, adsorbent load of 3.075 g/L, and T = 298.0 K. The kinetic study was evaluated using three models: a pseudo-first-order (PFO), a pseudo-second-order (PSO), and an intraparticle diffusion model. The observed kinetics is in excellent agreement with the PSO kinetic model. Therefore, both isotherms Langmuir and Freundlich fitted well the adsorption equilibrium data. The thermodynamic study revealed that the main parameters including (ΔG°, ΔH°, and ΔS°) indicated that the adsorption of CV onto NC was an endothermic and spontaneous process.

High-efficiency electrochemical degradation of phenol in aqueous solutions using Ni-PPy and Cu-PPy composite materials.

Conductive crystalline polypyrrole (Cryst-PPy), Nickel-polypyrrole (Ni-PPy), and copper- polypyrole (Cu-PPy) hybrid materials were prepared using a chemical polymerization method in an aqueous solution. Part I was focused on the Chemical synthesis of Cryst-PPy powder from an organic medium. Cryst-PPy powder was successfully synthesized by chemical route from an organic medium of acetonitrile with polyethylene oxide as a stabilizing agent and oxidizing agent like potassium peroxydisulfate. The morphological study was showed the presence of spherical nanoparticles and cubic microparticles giving rise to a denser structure of PPy. In the second part, the based electrodes composites were examined in the oxidation of phenol by an electrochemical process in an alkaline medium. To follow the yield of phenol degradation at the alkaline solution, UV-visible analysis was performed at the following operating conditions: current density of 0.58 mA cm-2, phenol initial concentration of 0.150 M and for 3 h processing; the rate of phenol elimination was 56%, 38% and 28% for Cu-PPy, Ni-PPy, and pure PPy electrodes respectively. Thus, can be found that the doped Cu-PPy electrodes electrode is a new material with high electrochemical oxidation ability for phenol degradation in aqueous solutions.

Optimization of thermostable proteases production under agro-wastes solid-state fermentation by a new thermophilic Mycothermus thermophilus isolated from a hydrothermal spring Hammam Debagh, Algeria.

The present work investigates for the first time the presence and isolation of the thermophilic fungi from hydrothermal spring situated at the locality of Guelma, in the Northeast of Algeria. The production of the thermostable proteases and the optimization of culture conditions under agro-wastes solid-state fermentation to achieve optimal production capacity were explored. A statistical experimental approach consisting of two designs was used to determine the optimum culture conditions and to attain the greatest enzyme production. Besides, different agricultural wastes were initially evaluated as a substrate, whereby wheat bran was selected for enzyme production by the isolate under solid-state conditions. The isolate thermophilic fungi were identified as Mycothermus thermophilus by sequencing the ITS region of the rDNA (NCBI Accession No: MK770356.1). Among the various screened variables: the temperature, the inoculum size, and the moisture were proved to have the most significant effects on protease activity. Employing two-level fractional Plackett-Burman and a Box-Behnken designs statistical approach helped in identifying optimum values of screened factors and their interactions. The analysis showed up 6.17-fold improvement in the production of proteases (~1187.03 U/mL) was achieved under the optimal conditions of moisture content 47%, inoculum 5 × 105 spores/g, and temperature at 42 °C. These significant findings highlight the importance of the statistical design in isolation of Mycothermus thermophilus species from a specific location as well as identifying the optimal culture conditions for maximum yield.

Photocatalytic degradation of Penicillin G in aqueous solutions: Kinetic, degradation pathway, and microbioassays assessment.

The photocatalytic degradation of pharmaceutical micropollutants of Penicillin G (PG) was investigated in a photoreactor at a laboratory scale. The impact of type of catalyst, pH, and initial concentration of PG were studied. Maximum removal efficiency was obtained at pH = 6.8, [ZnO]0 = 0.8 g L-1, and [PG]0 = 5 mg L-1 and reaction time of 150 min. The addition of persulfate sodium (PPS) enhanced the efficiency of the photocatalytic reaction. The efficiency of photolysis process in the presence of PPS was significantly improved to 72.72% compared to the classical photocatalysis system (56.71%). Optimum concentration of PPS to completely degraded PG was found to be 500 mg L-1. The QuEChERS extraction, GC-MS/MS method, and concentration technique showed favorable performance identification of the possible mechanism of PG degradation pathway. Toxicity of PG and its by-products were evaluated using microbioassays assessment based on nine selected bacterial strains. Results confirmed the effectiveness of the implemented system and its safe use via the bacteria Bacillus subtilis, which has illustrated significant activity. Due to the high efficiency, facility benefits, and low-cost of the suggested process, the process can be considered for the degradation of various pharmaceutical contaminants in pharmaceutical industry treatment under the optimal conditions.

Antimicrobial and antifungal properties of NiCu-PANI/PVA quaternary nanocomposite synthesized by chemical oxidative polymerization of polyaniline.

Increasing antimicrobial resistance has led to use of novel technologies such as nanomaterials and nanocomposites that have shown effective antimicrobial and/or antifungal activities against several gram-positive and gram-negative bacteria. There have been limited studies on antimicrobial properties of the combined polymer nanocomposites with transitional bimetallic nanoparticles such as nickel (Ni) and copper (Cu). Thus, the main objective of this study was to synthesis, characterize and investigate the antibacterial and antifungal properties of NiCu-PANI/PVA nanocomposite. The nanocomposite films with different amount of Ni and Cu salts were synthesized by chemical oxidative polymerization of polyaniline using HCl as oxidant and PVA as a stabilizer. Optical, chemical composition, and morphological characteristics as well as thermal stability were evaluated using UV-Visible, FTIR, SEM-EDX, and TGA analyses. Antimicrobial properties were then determined using the disc diffusion assay against gram-negative bacteria (i.e., Escherichia coli ATCC 25922, Klebsiella pneumonia ATCC 700603, Proteus sp.,) and gram-positive bacteria (i.e., Staphylococcus aureus ATCC 2593). Fungal plant pathogens including Aspergillus niger and Fusarium oxysporum f. sp. pisi were also evaluated for determination of antifungal activity of NiCu-PANI/PVA films. Among the synthesized films, Ni65Cu35-PANI/PVA showed excellent antibacterial activity against all the bacteria strains examined in this study. The diameters of inhibition zones for Escherichia coli ATCC 25922, Klebsiella pneumoniae ATCC 700603, Proteus sp., and Staphylococcus aureus ATCC 2593 were 23, 23, 17, and 18 mm, respectively indicating good antibacterial activities. Additionally, NiCu-PANI/PVA, particularly the films with higher Cu intake, showed better antifungal activity against Fusarium oxysporum f. sp. pisi. However, NiCu-PANI/PVA was ineffective against Aspergillus niger.

Recent advances in MXene-based nanomaterials for desalination at water interfaces.

The best exceptional Physico-chemical attributes of MXenes including high conductivity, high surface area, high functionalization, hydroxide site, and other interesting properties have attracted recently the attention of scientists in the applications of MXene (Mn+1XnTx)-based nanomaterials for water treatment. To provide a full and comprehensive vision of the current state of the art, and improve the treatment performance, and motivate new researches in this area, this review focused on the uses of these novel 2D transition metal carbides for desalination of water and the general methods of fabrication of MXenes; thus, MXene-based nanomaterials are very efficient candidates in water desalination processes, in this review, the main properties of previous and current works about MXenes applications in this area were properly investigated. Moreover, a particular overview about the different properties of MXenes in desalination such as etching method, hydrophobicity, structural modification, and chemical modification has been performed; meanwhile, the investigation of MXenes and MXenes-based composites would be an excellent candidate in the future of water purification and environmental remediation fields, since they have several good properties compared to the other 2D materials.

Biosynthesis, characterization, and evaluation of antibacterial and photocatalytic methylene blue dye degradation activities of silver nanoparticles from Streptomyces tuirus strain.

Silver nanoparticles (AgNPs) are a promising technology for the design of antimicrobial agents against drug-resistant pathogens. It could also be used for the photocatalytic degradation of dyes used in industries such as methylene blue (MB). In this study, 17 different actinomycetal strains isolated from hydrocarbon-contaminated soils collected from an oil distribution company in Algeria were evaluated for their ability to produce NPs. After a selection process, S16 was the main strain capable of synthesizing AgNPs extracellularly. The strain S16 was determined using molecular identification based on the sequencing of the 16S rDNA gene. Among various techniques used for the synthesis of AgNPS, a technique using a temperature of 30 °C, pH of 7, a metal salt concentration of 1 mM, and a period of 72 h in the dark were found to be more effective in the biosynthesis of the AgNPs. The biosynthesized AgNPs that were analyzed by UV-visible spectroscopy resulted in a specific peak at a wavelength of (λ = 400 nm). The DRX analyses showed characteristic peaks of the AgNPs at (1 1 1), (2 0 0), (2 2 2), and (3 1 1), which validated the presence and crystalline nature of the biosynthesized NPs. Zetasizer analysis showed an average size and zeta potential of 64 nm (-32.3 mV), while the SEM-EDS analysis confirmed the spherical shape of AgNPs and the presence of Ag atoms in the elemental composition. The biosynthesized AgNPs indicated adequate antibacterial activity against 5 out of the 6 strains tested in this study, using minimum inhibitory concentration (MIC) that ranged from 217.18 µg/mL to 1137.5 µg/mL. The AgNPs were combined with commercial antibiotics and the synergistic effect of the combination was also assessed against MRSA which resulted in increased antibacterial activity of AgNPs in the presence of the strain S16. Furthermore, the photocatalytic degradation of the methylene blue (MB) was evaluated under sunlight and UV irradiations using biosynthesized AgNPs. The AgNPs showed photocatalytic decolorization potential of 71.3% for MB dye (20 ppm) under sunlight irradiation within 6 h of incubation, while only 11.25% of the MB dye degraded using UV irradiation.

MXene-based electrochemical sensors for detection of environmental pollutants: A comprehensive review.

Since the discovery of MXenes at Drexel University in the United States in 2011, there has been extensive research regarding various applications of MXenes including environmental remediation. MXenes with a general formula of Mn+1XnTx are a class of two-dimensional (2D) transition metal carbides, carbonitrides, and nitrides with unique chemical and physical characteristics as nanomaterials. MXenes feature characteristics such as high conductivity, hydrophobicity, and large specific surface areas that are attracting attention from researchers in many fields including environmental water engineering such as desalination and wastewater treatment as well as designing and building efficient sensors to detect hazardous pollutants in water. In this study, we review recent developments in MXene-based nanocomposites for electrochemical (bio) sensing with a particular focus on the detection of hazardous pollutants, such as organic components, pesticides, nitrite, and heavy metals. Integration of these 2D materials in electrochemical enzyme-based and affinity-based biosensors for environmental pollutants is also discussed. In addition, a summary of the key challenges and future remarks are presented. Although this field is relatively new, future research on biosensors of MXene-based nanocomposites need to exploit the remarkable properties of these 2D materials.

The Fenton-like reaction for Arsenic removal from groundwater: Health risk assessment.

In this paper, the heterogeneous Fenton like-reaction for Arsenic-contaminated groundwater remediation based on the performance of FeSO4 as an efficient and green catalyst and CaO2 as a source of H2O2 was investigated. To intensify the heterogeneous Fenton process, three oxidants were tested: sodium percarbonate (SPC), sodium persulfate (SPS), and calcium peroxide (CP). The results showed that CP and SPC had a synergetic effect on the rate of Arsenic degradation, while SPS had an antagonistic effect. On the other hand, inorganic ions such as Na+, Mg2+ have a very low impact on the Arsenic removal efficiency, while the anions Cl- and NO3- exhibited significant inhibition of Arsenic degradation. This effect may be imputed to the reaction and conversion of hydroxyl (HO•) radicals to less reactive. Thus, HCO3- and humic acid dramatically raised the degradation rate. Also, the response Surface method based on Box-Behnken design was applied to examine the suitable modeling, and optimized condition of the Fenton like-reaction process, the maximum Arsenic removal efficiency of 94.91% is obtained when [Fe3+]0 = 1.97 mM, [CaO2]0 = 1.74 mM and initial pH = 4.67. The obtained results showed that the Fenton-like reaction is an effective and reliable process for arsenic removal from groundwater with low non-carcinogenic risk (HQ) and carcinogenic risk (CR) values.

Valorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp.

This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R2ANN = 0.884 and R2RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications.

A novel non-enzymatic glucose sensor based on NiFe(NPs)-polyaniline hybrid materials.

This article was focused on the elaboration of NiFe-Polyaniline glucose sensors via electrochemical technique. Firstly, the PANi (polyaniline) fibers were synthesized by oxidation of the monomer aniline on FTO (fluorine tin oxide) substrate. Secondly, the Nickel-Iron nanoparticles (NiFe (NPs)) were obtained by the Chronoamperometry method on the Polyaniline surface. The NiFe-PANi hybrid electrode was characterized by scanning electron microscopy (SEM), force atomic microscopy (AFM), Fourier-transformed infrared (FTIR), and X-ray diffraction (XRD). The electrochemical glucose sensing performance of the NiFe alloy nanoparticle was studied by cyclic voltammetry and amperometry. The fabricated glucose sensor Ni-Fe hybrid material exhibited many remarkable sensing performances, such as low-response time (4 s), sensitivity (1050 µA mM-1 cm-2), broad linear range (from 10 µM -1 mM), and low limit of detection (LOD) (0.5 µM, S/N = 3). The selectivity, reliability, and stability of the NiFe hybrid material for glucose oxidation were also investigated. All the results demonstrated that the NiFe-PANi/FTO hybrid electrode is very promising for application in electrochemical glucose sensing.

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.

Photocatalytic-persulfate- oxidation for diclofenac removal from aqueous solutions: Modeling, optimization and biotoxicity test assessment.

In this paper, the influence of several aquatic factors (the nature of catalyst, the initial pH and the initial concentration of the pollutant) on the photocatalytic degradation of diclofenac (DFC), one of the most widely prescribed anti-inflammatory non-steroidal drug, was studied. Also, in order to examine the intensification process, the variation of the photocatalytic DFC degradation in the presence of sodium persulfate (PPS) was analyzed. It was found that, compared to titanium dioxide (TiO2), the zinc oxide (ZnO) photocatalyst performed exceptionally well, with a 96.13% DFC degradation efficiency after 150 min. The photodegradation of DFC by ZnO catalyst fitted well the Langmuir-Hinshelwood kinetic model. The maximum efficiency is 97.27% for simulated solar-UVA/ZnO/PPS and 77% for simulated solar-UVA/ZnO. In order to determine the optimal conditions leading to the maximization of DFC removal, an artificial neural network (ANN) modeling approach combined with genetic algorithm (GA) was applied. The best ANN determined had a correlation of 0.999 and it was further used in the process optimization where a 99.7% degradation efficiency was identified as the optimum under the following conditions: DFC initial concentration 37,9 mg L-1, pH 5,88 and PPS initial concentration 500 mg L-1. The effectiveness of the process and the toxicity of the pharmaceutical pollutants and their by-products were also evaluated and confirmed by the biological tests using liver and kidney of Mus musculus mice.

Box-Wilson design modeling of photocatalytic degradation of industrial dye and wastewater in semi-pilot solar photoreactor.

This work focuses on the treatment of a dye solution, C.I. Basic Blue 41 (BB41), and industrial wastewater by UV/TiO2 photocatalytic process using aqueous catalyst suspensions of titanium dioxide (TiO2), Degussa P25. The procedures were carried out in a semi-pilot scale prototype solar photoreactor under solar radiation. Response surface methodology (RSM) based on Box-Wilson design was applied to assess individual effects of the five main independent parameters: initial dye concentration ([BB41]), TiO2 concentration ([TiO2]), flow rate (Q) initial pH and accumulated solar energy (Qvn) on the decolorization efficiency and to optimise the UV/TiO2 process. Photocatalytic mineralisation was carried out at the optimal conditions found by RSM and results were evaluated by total organic carbon (TOC) abatement for BB41 sloution and industrial wastewater. The optimal conditions found by RSM were: 0.4 g/L, 14.04 mg/L, 1,479.6 L/h, 5.52 and 80 KJ/L for TiO2 concentration, initial dye concentration, flow rate, initial pH and accumulated solar energy, respectively. Photocatalytic mineralisation results show that for accumulated visible solar energy equal to 377.714 kJ/L (after 6 hours of irradiation), under these conditions, the percentage of the initial TOC reduction is about 88% and 85.5% for industrial waste and BB41 solution, respectively.