Case Studies in Chemical and Environmental Engineering最新文献

In the present work, quinoxaline (0.002 M) as a corrosion inhibitor for copper in 1.5 M HNO₃ has been investigated at different temperatures. Weight loss, regression, and density functional theory (DFT) were used in the experimental, mathematical, and quantum chemical studies, respectively. Experimental studies show that the corrosion rate of copper increases with temperature, according to the Arrhenius equation. On the other hand, the percentage of inhibitor efficiency increased as temperature decreased, approaching a maximum value of 91 % at 25 °C. Kinetic studies showed that the corrosion reaction was zero-order. Corrosion rate data was fitted to a second-order mathematical model with a 0.974 correlation coefficient. The effect of inhibitor concentration on the corrosion rate was studied at low and high levels of temperature. The corrosion rate decreases with an increase in an increase in inhibitor concentration. The adsorption on the copper surface was spontaneous and followed the Langmuir adsorption isotherm. The theoretical quantum chemical calculation was used to support the experimental study. These calculations showed that the inhibitor molecules were the donors of electrons, while the metal surface was the acceptor. In addition, Mulliken charge data showed that the negative charges of quinoxaline are mainly concentrated on the nitrogen and carbon atoms. On the other hand, all hydrogen atoms have positive charges. This indicates a lack of hydrogen bond formation with the copper surface. This extensive study not only confirms quinoxaline’s efficacy as a corrosion inhibitor but also advances our knowledge of how it interacts with copper, opening the way to the creation of more focused and effective corrosion inhibitors that follow the rules of molecular design.

The synthesis of NS-CDs was carried out using precursors from Cavendish Banana Peel and l-Cysteine as a dopant with the solvothermal method. The characteristics of NS-CDs were analyzed through High-resolution transmission electron microscopy (HR-TEM), X-ray diffractometer (XRD), energy dispersive X-Ray spectroscopy (EDX), X-Ray Fluorescence spectrometer (XRF), X-Ray photoelectron spectroscopy (XPS), UV–Visible spectrophotometer, Photoluminescence, and Atomic Absorption Spectroscopy (AAS). Based on HR-TEM analysis, NS-CDs exhibited a spherical shape (dot) with an average particle size of 2.03 nm. Meanwhile, based on XRD characterization, NS-CDs showed a graphite carbon shape according to the diffraction patterns (002) and (001). Subsequently, XRF and EDX testing revealed that the elemental composition was dominated by carbon (C), nitrogen (N), Sulphur (S), and oxygen (O). Furthermore, in XPS testing, S2p, C1s, N1s, and O1s peaks correlated around 64 eV, 285 eV, 400 eV, and 531 eV respectively. In UV–Vis testing, the energy gap was found to be 5.71 eV (NS-CDs 3:1), 5.46 eV (NS-CDs 3:1), 5.25 eV (NS-CDs 1:1), 5.51 eV (NS-CDs 1:2), and 5.56 eV (NS-CDs 1:3). Characterization of PL for NS-CDs 3:1, 2:1, 1:1, 1:2, 1:3 showed peak excitation at 403 nm and emission at 493.39 nm, 493.65 nm, 494.98 nm, 496.04 nm, and 497.11 nm, respectively. During heavy metal ion detection testing, Fe(III) and Pb(II) using AAS instruments, it was found that the NS-CDs 1:3 sample yielded the best results with an Adsorption capacity worth 21.35 mg/L and Removal Efficiency worth 85.40 %. These results clearly indicate that NS-CDs material can be used as an ideal heavy metal detection material, especially in wastewater.

Metal Organic Framework (MOF) has great potential to be applied as an adsorbent in the removal of heavy metal ions Pb²⁺ in wastewater because it has a large specific surface area with abundant pores, as well as good chemical stability in aqueous systems. To improve its adsorption ability, MOF is incorporated with other materials to form a composite such as TiO₂. This study aims to synthesize a new MOF-based composite containing chromium metal and isonicotinic acid-modulated perylene organic linker (Cr-PTC-HIna) with TiO₂ loading using one-pot solvothermal for enhanced Pb²⁺ removal in aqueous systems. Then, the adsorbent materials were characterized by fourier transform infrared (FT-IR), X-ray diffraction (XRD), surface area analyzer (SAA), scanning electron microscopy-energy dispersive X-ray (SEM-EDX). TiO₂ loading created composites with larger surface area and greater adsorption capacity than Cr-PTC-HIna MOFs without TiO₂. The batch experiment exhibited The Cr-PTC-HIna composite with Cr and Ti mole ratio of 1:2 mmol showed an adsorption capacity of 135.14 mgg⁻¹ at 200 ppm Pb²⁺, pH 4, time 90 min and 298 K. The isotherm studies explained that Pb²⁺ was adsorbed through both physical and chemical interactions, followed the pseudo second-order model, and occurred exothermically, non-spontaneous. FT-IR and XRD investigations before and after use showed that the composites have good stability in aqueous solutions especially under acidic conditions, which implies that the developed composites provide enormous potential in the development of adsorbent materials for the removal of heavy metal ions in industrial wastewater which generally has a low pH value.

A series of bimetallic metal-organic frameworks (MOFs) photocatalysts had been successfully synthesized by doping nickel (Ni) into zeolitic imidazolate framework-8 (ZIF-8) for enhanced photodegradation of methylene blue (MB). Optical characterization revealed that the Ni-ZIF-8 composites possessed a narrower bandgap and lower recombination rate of charge carriers than pristine ZIF-8. The Ni(20)-ZIF-8 composite showed the highest MB photodegradation efficiency of up to 93.22 % in 150 min, with the use of 30 mg photocatalyst, 50 mL of 30 mg/L MB solution, and a neutral pH. The scavenger test clearly proved that h⁺ and •OH were the main active species.

Commonly used non-steroidal anti-inflammatory drugs (NSAIDs) can enter the soil via several routes. However, there have been relatively few studies on the impact of NSAIDs on the soil microbiome. Therefore, this study aimed to investigate the impact of Ibuprofen, Diclofenac, and their Mixture on the soil microbiomes of three different soil types (Cambic chernozem, Luvisols and Calcaric rendzinas). Changes in the soil microbiome profile were assessed using the phospholipid-derived fatty acid (PLFA) approach, as this method allows for the assessment of quantitative variations in the living soil microorganisms. The results showed that microbiome abundance fluctuates over time in the presence of both individual NSAIDs and mixtures. Cambic chernozem had a higher attenuation efficiency than Luvisols and Calcaric rendzinas. Principal component analysis showed that both fungal and bacterial phyla are affected by NSAIDs. The fungal community was more sensitive to NSAIDs than bacterial phyla in all soil types. Since Diclofenac and Ibuprofen were attenuated entirely at the experiment's end, we concluded that some species could use these NSAIDs as carbon or energy resources. The results of this study provide new insights into the response of the soil microbiome to non-target NSAID exposure.

The graphene oxide-silver nanocomposites (AgNPs@GO) have attracted much attention due to their targeted effect and nontoxicity against normal cells. The toxic factors, including the chemical reagents during the synthesis of AgNPs@GO, must be considered and minimized for the cancer treatment application. This study aims to apply the response surface methodology (RSM) with central composite design (CCD) for investigating independent factors, including mass ratio Ag⁺/GO, temperature, and reaction time, to find the optimal conditions for graphene oxide-silver nanocomposite (AgNPs@GO) synthesis. The green tea leaves extract was used as an alternative reducing agent for the green synthesis of AgNPs@GO. The maximum absorption wavelength of AgNPs, highly dependent on silver nanoparticles' size and shape, was utilized as the target function. The results show optimal conditions for the green synthesis of AgNPs@GO with the mass ratio of Ag⁺/GO is 2:1 at 30 °C for 40 minutes. The average maximum absorption wavelength of the synthesized AgNPs@GO at the optimal condition of 416.78 nm, with silver nanoparticles on the GO sheet having an average size of 21.2 ± 5.61 nm, the round shape and distributed on the GO sheets uniformly, without aggregation. In addition, the synthesized AgNPs@GO was against HepG2, MDA231, and MDA453, with the IC₅₀ of 152.582, 152.428, and 199.844 μg/mL, respectively.

Monitoring river water quality is crucial for safeguarding public health, protecting ecosystems, and ensuring economic sustainability. It helps detect contaminants, ensures drinking water safety, and facilitates early intervention for environmental protection and legal compliance. The objective of this study is to evaluate multiple machine learning algorithms to analyze water quality parameters in computing water quality index (WQI) and classification thereof, aiming to devise a reliable method for forecasting water quality with high accuracy. In this study, fourteen machine learning classifiers applied include Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naïve Bayes, Gradient boosting, AdaBoost, Bagging, Extra Trees, Quadratic Discriminant Analysis (QDA), XGBoost, and CATBoost. A total of 1096 sample data was used where each data consists of nineteen analytical water quality parameters. To assess the performance of various classifiers, several evaluation techniques were utilized including confusion matrices, classification reports detailing precision and accuracy ratios, and Receiver Operating Characteristic (ROC) curves. The study also utilizes explainable AI (LIME and SHAP) to provide clear insights into the decision-making processes used to classify river water quality. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. Among the classifiers used, Gradient Boosting achieves the highest Accuracy (99.64 %), Precision (0.95), Recall (0.96), and F1-Score (0.95), indicating its superior ability to correctly classify instances and suggesting a balanced performance across different evaluation metrics. The analysis presented in this article holds the promise of providing accurate water quality data to researchers, thereby enhancing monitoring effectiveness through the application of machine learning techniques.

Vanillin (4-hydroxy-3-methoxybenzaldehyde) is an important platform chemical compound that is invariably produced from pyrolysis of various categories of biomass. Hydrodeoxygenation (HDO) of vanillin has been a thematic topic in catalysis with the underlying aim to devise processes and reactions for catalytic upgrading of bio-oil and in the production of value-added products. Herein, we investigate the HDO of an evaporated stream of vanillin over a 4 % load of palladium supported on ceria; a Pd/CeO₂ catalyst. The synthesized catalyst was characterized by various techniques including XRD, XPS, EDX-SEM, HR-TEM, and TPR. The HDO reaction (carried out at temperatures from 100 °C to 300 °C at 10 °C/min ramp rate with a 5 % H₂ feed ratio) resulted in a 95 % conversion of vanillin with an 85 % yield of guaiacol (2-methoxyphenol). Minute loads of alkylbenzene compounds (mainly xylene and ethylbenzene) also emerged. DFT computations elucidate pathways for the observed formation of guaiacol where synergistic effects of both Pd and vacant oxygen sites are highlighted. Overall, we presented a viable HDO route for an oxygenated biomass model compound at mild operational conditions (intermediate temperatures, ambient pressure, and moderate H₂/Feed ratio).