Evaluation of Antibacterial and Antioxidant Activities of Silver‐Decorated TiO 2 Nanoparticles

Investigation of Doehlert matrix conception in novel intrinsically conducting polymers based on selenium nanoparticles for wastewater treatment: Synthesis, characterization, kinetic and chemometric study

The synthesis of robust intrinsically conducting polymers (ICPs) based on nanoparticles is becoming increasingly attractive to the research community due to the unique properties of these nanocomposites. Indeed, as organic semiconductors, ICPs combine both polymer and metal properties in a single structure. This study presents an innovative approach in which the Doehlert Matrix (DM) is applied to a novel ICP nanocomposite based on polyaniline (Pani) coupled with selenium (Se) loaded mesoporous titania (TiO2) for wastewater treatment by photocatalysis. It includes both the elaboration routes of ICP nanocomposites, characterization of materials by X-ray diffraction (XRD), BET analysis, thermogravimetric analysis (TGA), RAMAN spectroscopy and Fourier transform infrared spectroscopy (FTIR) and photodegradation of methylene blue (MB) as a representative of dye pollutant. In addition, the photocatalytic process has been optimized by a novel DM conception. The effect of the pH of the solution, the catalyst dosage and the initial pollutant concentration was investigated. The optimum conditions were found to be: initial MB concentration of 15 mg/L, the catalyst dosage of 69 mg and pH of 9.6 with an operating time of 75 min, with a coefficient of determination R2 equal to 0.9985. The removal efficiency of BM was close to 97 %. The study shows that the new ICP nanocomposites improve the photocatalytic efficiency compared to pure titania and/or pure Pani. In addition, as the ternary Pani-Se-TiO2 nanocomposite could be obtained from a low-cost synthesis, it is a very promising material for use in wastewater treatment.

Analysis of Ag-DP25/PET plasmonic nano-composites as a visible-light photocatalyst for wastewater treatment: Experimental/theoretical studies, and the DFT-MB degradation mechanism

The development of polymeric-composites Agx%DP25-PET (x = 0,1,2,3) may significantly boost the potential application of Agx%DP25 (x = 0,1,2,3) photocatalytic powders. Producing large-scale nano-composites with hybrid-surfaces, that are also flexible materials and easy to employ in a variety of environments. A set of photocatalytic nan-composites embedded with the polymeric binder poly (acrylonitrile-co-butadiene)-dicarboxy terminated (C7H9N) were performed and evaluated for wastewater treatment applications. The results reveal that the flexible polymeric composites (Agx%DP25-PET, x = 0,1,2,3) have photocatalytic activity in aqua media to degrade methylene blue (MB) under visible-light. The addition of C7H9N to immobilize photocatalytic powders on the PET surface reduces photo-generated electron-hole recombination. The materials were characterized by HR-TEM, SEM/EDX, XRD, FT-IR, UV-Vis DRS and PL. The Agx%DP25-PET (x = 0,1,2,3) photocatalytic reactions exhibited productive discoloration/degradation rates, in both aerobic (AE) and anaerobic (AN) environments. The superior photodegradation of Ag2%DP25-PET was attributed to a combination of two effects: LSPR (localized surface plasmon resonance) and Ag-TiO2/environment affinities. The findings of molecular dynamics (MD) simulation and Fukui Function (FF) based on density functional theory (DFT) provide significant insight into the photocatalytic requirements for MB discoloration/degradation. The experimental/theoretical analysis aimed to offer an in-depth understanding of medium/surface interactions on decorated TiO2 materials, as well as how these interactions affect overall degradation behavior.

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.

A critical review on the recent trends of photocatalytic, antibacterial, antioxidant and nanohybrid applications of anatase and rutile TiO2 nanoparticles

Titanium dioxide nanoparticles (TiO2 NPs) have become a focal point of research due to their widespread daily use and diverse synthesis methods, including physical, chemical, and environmentally sustainable approaches. These nanoparticles possess unique attributes such as size, shape, and surface functionality, making them particularly intriguing for applications in the biomedical field. The continuous exploration of TiO2 NPs is driven by the quest to enhance their multifunctionality, aiming to create next-generation products with superior performance. Recent research efforts have specifically focused on understanding the anatase and rutile phases of TiO2 NPs and evaluating their potential in various domains, including photocatalytic processes, antibacterial properties, antioxidant effects, and nanohybrid applications. The hypothesis guiding this research is that by exploring different synthesis methods, particularly chemical and environmentally friendly approaches, and incorporating doping and co-doping techniques, the properties of TiO2 NPs can be significantly improved for diverse applications. The study employs a comprehensive approach, investigating the effects of nanoparticle size, shape, dose, and exposure time on performance. The synthesis methods considered encompass both conventional chemical processes and environmentally friendly alternatives, with a focus on how doping and co-doping can enhance the properties of TiO2 NPs. The research unveils valuable insights into the distinct phases of TiO2 NPs and their potential across various applications. It sheds light on the improved properties achieved through doping and co-doping, showcasing advancements in photocatalytic processes, antibacterial efficacy, antioxidant capabilities, and nanohybrid applications. The study concludes by emphasizing regulatory aspects and offering suggestions for product enhancement. It provides recommendations for the reliable application of TiO2 NPs, addressing a comprehensive spectrum of critical aspects in TiO2 NP research and application. Overall, this research contributes to the evolving landscape of TiO2 NP utilization, offering valuable insights for the development of innovative and high-performance products.

The effect of TiO2 nanoparticles on bacterial growth: the effect of particle size and their structure - a systematic review

One of the widely used microbiological methods to determine the toxicity of chemicals, catalysts, and other types of materials is the minimum inhibitory concentration (MIC) test. The present study aims to investigate the influence of composition of composite materials based on TiO2 and their particle size as well as bacterial type and shape based on the MIC values reported in the literature. The results show that among the 36 articles selected, most of the studies used Escherichia coli (E. coli) (26) and Staphylococcus aureus (S. aureus) (19) bacteria to determine MIC values. This study revealed that the MIC in values below 70 µg ml-1 for S. aureus was lower than that for E. coli bacteria (below 200 µg ml-1). Importantly, MIC value decreased from 60.6 to 7.66 µg ml-1 with decrease in the size of nanoparticles. It follows from the increased surface area for smaller-sized particles, thus increased interaction with bacteria during MIC test.

Cefotaxime degradation by the coupled binary CdS-PbS: characterization and the photocatalytic process kinetics

Increased water pollution due to discharging industrial/urban/hospital wastewater has been adopted to introduce/develop novel removal techniques/catalyst/adsorbent. The hexagonal (wurtzite) CdS and the cubic PbS nanoparticles (NPs) were synthesized, coupled, and supported onto clinoptilolite NPs (CNP). Then, the sample was characterized by X-ray powder diffraction (XRD), diffuse reflectance spectroscopy (DRS), Fourier transform infrared (FTIR), and a scanning electron microscope equipped with an energy dispersive X-ray analyzer (SEM-EDX) techniques. The average crystallite size for CdS NPs, PbS NPs, CNP, and CdS-PbS/CNP samples was obtained at about 24, 36, 27, and 14 nm using the Scherrer formula value of nanometer, by the W-H formula, 31, 17, 39, and 51, respectively. Only a detectable slope can be observed from the DRS spectra for CdS NPs at 591 nm corresponding to an Eg value of 2.1 eV. PbS NPs have a broad abruption peak that begins from the visible region and extends to the IR region of the light. A boosted photocatalytic activity of the supported binary catalysts towards cefotaxime (CT) was reached. An apparent first kinetic model was reached with a k-value of 0.021 min^-1 corresponding to the t_1/2 value of 33 min. A decreased COD trend for the photodegraded CT solutions was reached, and the chemical oxygen demand (COD) results in the Hinshelwood model showed a k-value of 0.016 min^-1, corresponding to a t_1/2 value of 43 min.

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.

A designed experiment for CdS-AgBr photocatalyst toward methylene blue

A boosted photocatalytic activity was observed for the CdS-AgBr nanocomposite in the degradation of methylene blue (MB). The experimental design method based on the response surface methodology (RSM) approach used to study the simultaneous interaction effects between the influencing variables. Analysis of variance (ANOVA) of the results confirmed a significant model for processing the data because an F value of 32.34 for the suggested model was higher than that of the critical value of F_{0.05, 14, 13} = 2.55 at 95% confidence interval. This analysis also showed a non-significant lack of fit (LOF) (as a measure of the randomness of the deviations around the obtained data) because the LOF F value of 8.27 was smaller than that of the critical value of F_{0.05, 10, 3} = 8.79. R² values near to unity were achieved (the multiple correlation coefficients R² (R² = 0.9627), adjusted R² (adj-R² = 0.9226), and predicted R² (pred-R² = 0.7423)). Six center points suggested by the model included the following conditions: pH, 6.1; C_MB, 3.5 mg/L; a dose of the catalyst, 0.68 g/L; and irradiation time, 40.5 min. During the center point runs, the degradation efficiencies were obtained in the range of 38 to 43%. The optimal run included pH, 9; catalyst dosage, 1 g/L; irradiation time, 60 min; and C_MB, 2 mg/L, and the best removal efficiency of 98% was achieved during these conditions.

Synthesis, characterization, and comparative assessment of antimicrobial properties and cytotoxicity of graphene-, silver-, and zinc-based nanomaterials

Zinc oxide (ZnO) and graphene oxide (GO) nanoparticles, silver/zinc zeolite (Ag/Zn-Ze), and graphene oxide-silver (GO-Ag) nanocomposites were synthesized and characterized with X-ray powder Diffraction, Field Emission Scanning Electron Microscope and Fourier Transform-Infrared Spectroscopy. The antibacterial efficacy of these nanoparticles was evaluated against E. coli. by shake flask method and plate culture method for different concentrations. For 105 cells/mL initial bacterial concentration, minimum inhibitory concentration (MIC) were <160, <320, <320, and >1280 μg/mL, and antibacterial concentration at which 50% cells are inhibited (IC50) were 47, 90, 78, and 250 μg/mL for Ag/Zn-Ze, GO, GO-Ag, and ZnO, respectively. Therefore, the shake flask method showed that for all nanoparticle concentrations, Ag/Zn-Ze, and GO-Ag exhibited greater inhibition efficacy, which was also highly dependent on initial bacterial concentration. However, in case of the plate culture method, similar range of inhibition capacity was found for Ag/Zn-Ze, GO-Ag, and ZnO, whereas GO showed lower potency to inhibit E. coli. In addition, GO-Ag nanocomposite exhibited more efficacy than Ag/Zn-Ze when the antibacterial surface was prepared with those. However, Ag/Zn-Ze showed no toxicity on Vero cells, whereas GO-Ag exhibited severe toxicity at higher concentrations. This study establishes GO-Ag and Ag/Zn-Ze as potent antimicrobial agents; however, their application dosage should carefully be chosen based on cytotoxic effects of GO-Ag in case of any possible physiological interaction.