Cadmium sulfide nanoparticles: Synthesis, brief characterization and experimental design by response surface methodology (RSM) in the photodegradation of ranitidine hydrochloride

Isophthalate and N-Chelating Linker-Based Luminescent Cd-MOF for "Turn-On" EtOH Sensing via RGB-Assisted Smartphone Platform and "Turn-Off" Fe3+ Monitoring in Biodiesel Specimens

Monitoring trace solvent and metal adulteration in biodiesel is crucial for quality control and commercialization. This study explores a mesoporous Cd(II)-MOF for dual-mode fluorescence sensing: "turn-on" detection of EtOH and "turn-off" monitoring of Fe3+ in aqueous and biodiesel samples. Synthesized using N-chelating, π-conjugated 2,2'-bipyridine and μ3-η1,η1,η2 bridging 5-hydroxyisophthalic acid, the MOF exhibits high phase purity, lamellar rod morphology, and high thermal stability, attributed to its robust framework, reinforced by supramolecular H-bonding and interlayer π-π stacking interactions. The MOF's blue luminescence enables rapid detection of EtOH (6.27-fold enhancement, LOD: 6.33 ppm, 40s response time) and Fe3+ (>90% quenching, LOD: ≈1.17 μM, KSV: 1.318 × 105 M- 1, 30 s response time). The EtOH sensing ensues via restriction of photo-induced electron transfer in the MOF, aided by hydrogen bonding with MOF hydroxyls and polarity effects, while Fe3+ quenching arises from absorption competition quenching and electrostatic interactions. The sensor detects EtOH and Fe3+ in pretreated biodiesels derived from Jatropha and waste cooking oil (recovery: 78-90% and 73-75%, respectively). Further, a 4.89-fold "turn-on" for EtOH and 81% quenching for Fe3+ is evidenced when analytes are directly added into untreated Jatropha biodiesel. A smartphone-based RGB calibration further enhances real-time ethanol analysis, ensuring practical applicability.

Comparison of Photocatalytic and Adsorption Properties of ZnS@ZnO, CdS@ZnO, and PbS@ZnO Nanocomposites to Select the Best Material for the Bifunctional Removal of Methylene Blue

In this study, photocatalytic- and adsorption-based removal processes were conducted, which are frequently preferred in wastewater treatment due to their ease of control and high removal efficiency. An innovative method aimed at wastewater treatment was developed by combining the advantages of these two distinct approaches within the same material. The study synthesized ZnO, ZnS, CdS, PbS, and their composite structures (ZnS@ZnO, CdS@ZnO, and PbS@ZnO) using a hydrothermal synthesis method. Characterization of the samples was performed through field emission-scanning electron microscopy (FE-SEM), FE-SEM-energy dispersive X-ray (FE-SEM-EDX), X-ray diffraction (XRD), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR) measurement. Additionally, the optical properties of all samples (absorption spectra and band gap) were investigated by using absorbance measurements obtained from ultraviolet (UV)-visible absorption spectroscopy. Although ZnO nanoparticles are among the materials with high photocatalytic properties (exhibiting a photodegradation efficiency of 95.8% in a short duration of 90 min), their adsorption properties are low. Therefore, with the aim of enhancing both the low adsorption values and the photocatalytic properties of pure metal sulfides (ZnS, CdS, PbS), nanocomposites ZnS@ZnO, CdS@ZnO, and PbS@ZnO with different morphologies were synthesized, and their photocatalytic and adsorption-based removal performances on methylene blue (MB) dye were investigated. FE-SEM images indicated that ZnS nanoparticles exhibit a spherical morphology, CdS nanoparticles have a flower-like morphology, and PbS nanoparticles display a dendritic-like structure. The results obtained from experimental studies demonstrated that the highest efficiency in both photocatalytic- and adsorption-based removal was achieved with the ZnS@ZnO nanocomposite. The degradation rates of MB were found to be 95.3, 90.5, and 89.4% for the heterojunction composites ZnS@ZnO, CdS@ZnO, and PbS@ZnO, respectively, over a time range of 0-480 min. The optimal amount of photocatalyst that could effectively degrade MB was determined to be 100 mg, and the reusability studies revealed that the ability of the ZnS@ZnO semiconductor heterojunction photocatalyst to decompose MB into simpler molecules was limited after the fourth cycle. The adsorption-based removal rates were 96.0, 30.5, and 19.4% for the heterojunction composites ZnS@ZnO, CdS@ZnO, and PbS@ZnO, respectively. Finally, parameters influencing the adsorption-based removal of MB, such as pH, mass, and contact time, were examined, indicating that the adsorption capacity of ZnS@ZnO remained unchanged after reaching a value of 40 mg·g-1.

Mechanisms in the photocatalytic breakdown of persistent pharmaceutical and pesticide molecules over TiO2-based photocatalysts: A review

Titanium dioxide (TiO2) based photocatalysts have been widely used as a photocatalyst for the degradation of various persistent organic compounds in water and air. The degradation mechanism involves the generation of highly reactive oxygen species, such as hydroxyl radicals, which react with organic compounds to break down their chemical bonds and ultimately mineralize them into harmless products. In the case of pharmaceutical and pesticide molecules, TiO2and modified TiO2photocatalysis effectively degrade a wide range of compounds, including antibiotics, pesticides, and herbicides. The main downside is the production of dangerous intermediate products, which are not frequently addressed in the literature that is currently available. The degradation rate of these compounds by TiO2photocatalysis depends on factors such as the chemical structure of the compounds, the concentration of the TiO2catalyst, the intensity, the light source, and the presence of other organic or inorganic species in the solution. The comprehension of the degradation mechanism is explored to gain insights into the intermediates. Additionally, the utilization of response surface methodology is addressed, offering a potential avenue for enhancing the scalability of the reactors. Overall, TiO2photocatalysis is a promising technology for the treatment of pharmaceutical and agrochemical wastewater, but further research is needed to optimize the process conditions and to understand the fate and toxicity of the degradation products.

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L-1, 3.13, 117.65 min, and 0.19 g L-1) and (5 mg L-1, 3, 120 min, and 0.19 g L-1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

Persulfate activation with sodium alginate/sulfide coated iron nanoparticles for degradation of tetrabromobisphenol a in soil

The accumulation of tetrabromobisphenol A (TBBPA) in soil posed a serious threat to ecosystem and human health. Sodium alginate/sulfide coated iron nanoparticles (SA@S-Fe NPs) was synthesized by a two-step modification of Fe NPs prepared with tung tree leaves extracting solution, and utilized as a persulfate (PS) activator to degrade TBBPA in soil. Response surface methodology (RSM) optimization showed a theoretical maximum TBBPA degradation reaching 99.79% at the 34.28 °C, SA@S-Fe NPs and PS additions of 3.57 g kg^-1 and 36.35 mM, respectively. The degradation mechanism of TBBPA suggested that the main reactive species produced in the SA@S-Fe NPs/PS system were •OH, SO₄^•-, and O₂^•-. Proposed mechanisms for the degradation of TBBPA in soil involved debromination, benzene rings split, hydroxylation, demethylation, and complete mineralization to CO₂ and H₂O. We also further studied the effect to soil physicochemical properties and morphology structure during TBBPA degradation in SA@S-Fe NPs/PS system, which showed that SOM, TN, C/N and TOC slightly reduced, the heavy metals Fe, Cu and Zn still existed in stable residue form, and the soil morphology showed a certain degree of aggregation. Therefore SA@S-Fe NPs/PS technology can effectively degrade soil TBBPA, maintain soil fertility, curb the migration of heavy metals, and environmental risks.

Optimized synthesis of mono and bimetallic nanoparticles mediated by unicellular algal (diatom) and its efficiency to degrade azo dyes for wastewater treatment

The silver/palladium nanoparticles (Ag/Pd NPs) were efficiently absorb UV-Visible light and reveal greater photocatalytic activity as compared to monometallic NPs. The aim of this study is photodegradation of the industrial azo dye using bimetallic Ag/Pd NPs and monometallic Ag NPs in presence of UV light for wastewater treatment. Bacillariophyceae (diatom) algae extract was utilized for the green synthesized Ag and Ag/Pd NPs. Biosynthesized nanoparticles were characterized by various useful characterization techniques viz. UV-Vis, FT-IR, SEM, TEM, and XRD. The crystallite size is found to be ∼23 nm and ∼56 nm for Ag NPs and Ag/Pd NPs, respectively, which is same as results obtained from TEM analysis, as the particle size and shape were analyzed as ∼27 and ∼56 nm, with a spherical geometry. The NPs was used to develop the optimization parameters for dye degradation such as time, temperature, and NP concentrations. A total 15 runs were considered for the study and procured by statistical software. Response surface methodology technique was implied and Box-Behnken design (BBD) design was built into the workflow. The results of the present study manifested a good connection between experimental and predicted values (R² = 0.9838). Therefore, present study promises that the prepared NPs possess excellent photocatalytic activity against harmful dyes.

Application of the Response Surface Methodology (RSM) in the Optimization of Acenaphthene (ACN) Removal from Wastewater by Activated Carbon

The presence of polycyclic aromatic hydrocarbons (PAHs) in wastewater has raised concerns about human health due to their potential carcinogenic and mutagenic properties. The widespread use of products containing acenaphthene (ACN, one of the 16 priority PAHs) in many industries and large-scale ACN release into the wastewater has resulted in dangerous concentrations of ACN in the environment. As a result, before discharge, it is required to eliminate or reduce its concentration to an acceptable level. Adsorption is an effective method of removing PAHs from wastewater. In this study, the ACN adsorption reaction in sample wastewater was evaluated using activated carbon produced by oil palm leaves. HPLC was used as an analytical method for quantifying ACN in wastewater samples. The initial concentration of ACN in water samples was 9.58 ± 0.5 mg/L. The experiments were conducted using the CCD combined with the RSM and using three independent variables, i.e., pH, activated carbon dosage (g/L), and contact time (min), and one dependent variable, i.e., ACN removal efficiency (%). The ANOVA was used to identify the significance of the developed model in the RSM. Lastly, the RSM was used to optimize the adsorption results. The experimental results determined that the removal of 98.73 ± 1% of ACN (the highest amount) was achieved at pH 7, while the removal of 88.44 ± 1% of ACN (the lowest amount) was achieved at pH 4.5. The adsorption efficiency of ACN was slightly increased by an increase in activated carbon dosage from 0.1 to 3 g/L (<4%). The contact time was the most significant factor in controlling the adsorption efficiency of ACN in wastewater, and not pH value or dosage. The adsorption reaction was quick, and 88–90% of ACN was removed within 5 min of the adsorption reaction, followed by slower adsorption for up to 90 min. The RSM model was developed on the basis of experimental results. An ANOVA determined that the developed model was significant enough to represent the adsorption data as the p-value was <0.05 for the model. The factors pH, adsorbent dosage, and contact time were also significant factors (p-value < 0.05). The optimization results showed that pH of 6.96, adsorbent dosage of 2.62 g/L, and contact time of 71.67 min were the optimal conditions for eliminating 98.88% of the ACN. The optimization results were verified in the lab, and a close agreement was found between the predicted results of the RSM and experimental results. The study found that the RSM is an effective tool for optimizing operating variables, as well as for significantly reducing time and experimentation costs.

Optimization of the Synthesis of Fungus-Mediated Bi-Metallic Ag-Cu Nanoparticles

Bi-metallic nanoparticles (NPs) have appeared to be more efficient as antimicrobials than mono-metallic NPs. The fungus Aspergillus terreus-mediated synthesis of bi-metallic Ag-Cu NPs was optimized using response surface methodology (RSM) to reach the maximum yield of NPs. The optimal conditions were validated using ANOVA. The optimal conditions were 1.5 mM total metal (Ag + Cu) concentration, 1.25 mg fungal biomass, 350 W microwave power, and 15 min reaction time. The structure and shape of the synthesized NPs (mostly 20–30 nm) were characterized using several analytical tools. The biological activities of the synthesized NPs were assessed by studying their antioxidant, antibacterial, and cytotoxic activity in different NP concentrations. A dose-dependent response was observed in each test. Bi-metallic Ag-Cu NPs inhibited three clinically relevant human pathogens: Klebsiella pneumoniae, Enterobacter cloacae, and Pseudomonas aeruginosa. Escherichia coli, Enterococcus faecalis, and Staphylococcus aureus were inhibited less. The DPPH and hydrogen peroxide scavenging activities of the NPs were high, reaching 90% scavenging. Ag-Cu NPs could be studied as antimicrobials in different applications. The optimization procedure using statistical analyses was successful in improving the yield of nanoparticles.