Influence of operational parameters on photocatalytic decolorization of a cationic azo dye under visible-light in aqueous Ag3PO4

New magnetic nanocomposites based on hexafrite and keggin-type -type heteropolyanions: Synthesized and characterized for removal of environmental pollutants

This research paper details the creation of innovative nanocomposites using the sol-gel technique, incorporating polyoxometalates SiW9Ba3 to stabilize ceramic particles of strontium ferrite (SrFe12O19) polymer and Chitosan (CS). The identification and confirmation of the nanocomposites obtained at each stage were carried out through the use of FT-IR, EDX, XRD, and FESEM analyses. To evaluate their ability to remove organic dyes, we analyzed the catalytic activity of these nanocomposites during photocatalytic detoxification procedures. With its exceptional photocatalytic properties, the nanocomposite (SiW9Ba3@SrFe12O19@Cs) was able to remove estamipride poison at an impressive rate of 85 % and xylene dye solution at an even higher rate of 98 %. In addition, an extensive examination was undertaken to explore the primary variables that influence process efficiency. The study suggests that ceramic nanocomposites incorporating heteropolyoxometalate may offer a viable approach to effectively eradicate pollutants from the environment.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R², MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

A novel surface imprinted resin for the selective removal of metal-complexed dyes from aqueous solution in batch experiments: ACB GGN as a representative contaminant

Metal-complexed dyes are harmful to the environment and human health because they contain heavy metals and complex organic ligands. It is difficult to separate and recover these dyes from wastewater owing to their complex components and poor selectivity of common adsorbents. In this study, a novel surface molecularly imprinted polymer (SMIP) was prepared using 4-vinyl pyridine as the functional monomer and polystyrene resin as the carrier. SMIP showed better adsorption performance than non-imprinted polymer (SNIP) in the whole pH range with the best adsorption capacity at pH 1.5. The correlation coefficients (R²) fitted by Langmuir and Temkin models were greater than 0.97, and the adsorption was a spontaneous exothermic process. The pseudo-second-order and Elovich models fitted the adsorption kinetic curves well. The adsorption capacity of SMIP was approximately 20% higher than that of SNIP in the salt concentration ranging from 2 to 80 mg/L. In selective adsorption experiments, the relative selectivity coefficients (I) of SMIP for competitors were all greater than 2.41, and the Cr (Ⅲ) components of ACB GGN played a more important role in the recognition performance of SMIP than the sulfonic groups. Adsorption mechanism tests revealed that although the adsorption of ACB GGN by SMIP mainly relied on electrostatic attraction, hydrophobic interactions, π-π conjugation, and Cr (Ⅲ) coordination were also involved. These results show that SMIP has excellent selective adsorption properties for ACB GGN and a promising application potential in the treatment of metal-complexed dye wastewater.

Photocatalytic Properties of Core-Shell Structured Wool-TiO2 Hybrid Composite Powders

In this study, a special core–shell structured wool-TiO2 (WT) hybrid composite powder also having TiO2 nanoparticles incorporated inside cortical cells was reported. The wool pallets were pulverized from wool fibers using vibration-assisted ball milling technique and the WT powders having mesopores and macropores were produced in hydrothermal process. Experimental results indicated that the infiltrated TiO2 nanoparticles were amorphous structure, while the coated TiO2 nanoparticles were anatase phase structure. The crystallized TiO2 nanoparticles were grafted with wool pallets by the N−Ti4+/S−Ti4+/O−Ti4+ bonds. The BET surface area was measured as 153.5 m2/g and the particle sizes were in the 600–3600 nm and 4000–6500 nm ranges. The main reactive radical species of the WT powders were holes, and •O2−, 1O2, and •OH were also involved in the photodegradation of MB dye under visible light irradiation. The experimental parameters for photodegradation of MB dye solution were optimized as follows: 0.25 g/L of WT powders was added in 40 mL of 3 mg/L MB dye solution containing 50 mL/L H2O2, which resulted in the increases of COD value of degraded MB dye solution up to 916.9 mg/L at 120 min. The WT powders could be used for repeatedly photodegradation of both anionic and cationic dyes.

Enhanced Photodegradation of Synthetic Dyes Mediated by Ag3PO4-Based Semiconductors under Visible Light Irradiation

Four silver phosphate-based materials were successfully synthesized, characterized, and evaluated, together with TiO2, in the photodegradation of synthetic dyes (tartrazine, Orange II, rhodamine, and Brilliant Blue FCF) under two irradiation sources centered at 420 and 450 nm. Scanning Electron Microscopy (SEM) images showed different topologies of the synthesized materials, whereas diffuse reflectance spectra demonstrated that they display absorption up to 500 nm. Degradation experiments were performed in parallel with the silver materials and TiO2. Upon irradiation centered at 420 nm, the abatement of the dyes was slightly more efficient in the case of TiO2—except for Orange II. Nevertheless, upon irradiation centered at 450 nm, TiO2 demonstrated complete inefficiency and silver phosphates accomplished the complete abatement of the dyes—except for Brilliant Blue FCF. A careful analysis of the achieved degradation of dyes revealed that the main reaction mechanism involves electron transfer to the photogenerated holes in the valence band of silver photocatalysts, together with the direct excitation of dyes and the subsequent formation of reactive species. The performance of TiO2 was only comparable at the shorter wavelength when hydroxyl radicals could be formed; however, it could not compete under irradiation at 450 nm since the formed superoxide anion is not as reactive as hydroxyl radicals.