Journal of the Iranian Chemical Society最新文献

The objective of this study was to assess the capacity of Anionic Polyacrylamide (APAM) to remove heavy metal ions, such as cadmium and lead, from aqueous solutions through adsorption. A customized design based on response surface methodology (RSM) was used to build predictive models and optimize heavy metal reduction. Twenty experiments were conducted to evaluate the impact of variables on the adsorption removal process of both heavy metals, including the initial concentration (IC) (A), adsorbent dose (B), and pH (C), as well as their interactions. The results indicate that increasing the pH and adsorbent dose leads to a higher removal rate for both metals, whereas increasing the initial concentration of the metals results in a lower removal rate. The interaction between variables was found to be insignificant. The statistical analysis demonstrates that both heavy metal models are highly significant with very low probability values (p < 0.0001) and excellent predictability, with R² values exceeding 0.91 for the two metals. The optimal values for initial concentration, adsorbent dose, and pH for cadmium and lead were determined to be 10.29-10 ppm, 0.063 –0.026 g, and 4.49-5, respectively. Under these optimal conditions, the removal efficiency for cadmium and lead was found to be between 99% and 99.33%, respectively. The Langmuir model described best the equilibrium adsorption behavior, indicating a monolayer adsorption with maximum adsorption capacities of 418.04 mg/g for Pb²⁺ and 236.48 mg/g for Cd²⁺, while the pseudo-second-order model provided the best fit for analyzing the kinetics of adsorption. These findings highlight APAM as a highly effective and promising adsorbent for heavy metals remediation, which offer a viable solution for water treatment.

Electrolyte and metabolic homeostasis dysregulation is a common and clinically significant complication in cancer patients, impacting prognosis and treatment efficacy. A comprehensive assessment of the serum ion profile can provide valuable insights into pathophysiological processes. Capillary zone electrophoresis (CZE) offers a promising alternative to traditional methods like ion chromatography, owing to its high speed, separation efficiency, small sample volume, and simplified sample preparation. The aim of the study is to evaluate the content of major inorganic cations (K⁺, Na⁺, Mg²⁺, Ca²⁺), anions (Cl⁻, SO₄²⁻, HPO₄²⁻, citrate, lactate), and their ratios in the blood serum of patients with lymphoproliferative diseases (LPDs) compared to healthy donors using CZE. The study involved 85 participants: 31 patients diagnosed with Hodgkin lymphoma (HL) and non-Hodgkin lymphomas (NHL), and 54 healthy donors. Analyte concentrations in serum samples were determined by CZE. Statistical analysis was performed to identify significant differences between groups. Patients with LPDs exhibited statistically significant alterations in their ion profile compared to healthy donors. Elevated concentrations of potassium (p < 0.0001), sulphates(p = 0.0167), and lactate (p < 0.00001) were observed, alongside a decrease in ionized calcium levels (p = 0.0253). Consequently, a significant reduction in the Na/K (p = 0.0013) and Ca/Mg (p = 0.0315) ratios was noted in the patient group. Comparison of patient subgroups showed that there were no statistically significant differences in the content of the studied analytes in patients with NHL and HL. The findings indicate a profound and complex dysregulation of electrolyte and metabolic homeostasis in patients with lymphoproliferative diseases. The identified changes, particularly elevated lactate and potassium levels, and altered ion ratios, can serve as additional biochemical markers for assessing disease severity and monitoring patient status. The CZE method demonstrated its effectiveness for rapid and accurate multicomponent analysis of blood serum.

Various types of pesticides have been utilised nowadays to increase yield by controlling pests. Organophosphorus pesticides are one of the most commonly used classes of pesticides. The constant use of these pesticides has contaminated the environment, and possibly put human health at risk. However, pesticide levels in different matrices are generally below the limits of detection for many analytical instruments and have high matrix interference, making them impossible to analyze directly from samples. In order to maximize the analytical performance of highly sensitive and sophisticated instruments and to obtain trustworthy analytical results, sample preparation is a critical step in the development of pesticide residue analysis. This review describes the sample preparation techniques of two broad category namely solvent extraction techniques and sorbent extraction techniques that have been proposed since 2016 for the extraction of globally used organophosphorus pesticides. The key features of various sample preparation techniques are discussed with an emphasis on optimal parameters and notable advantages-disadvantages. Based on the present review, QuEChERS and LPME emerge as the most widely employed sample preparation techniques for the analysis of organophosphorus pesticides. QuEChERS is predominantly preferred for food matrices due to its high throughput, simplicity and efficiency. SBSE is the choice of approach for environmental samples while LLE and its miniaturization techniques have been successfully utilized for the extraction of OPPs from lipid-rich matrices. Furthermore, the development of novel functionalized sorbent materials has expanded the applicability of various solid-phase extraction formats, making them increasingly attractive within the framework of green analytical chemistry. It seems possible to extend these approaches to extract and cleanup the compounds of interest in other similar matrices as well. The review serves as a valuable resource for method development and future studies, as the important parameters of sample preparation procedures are compiled from the relevant publications.

In this study, nonperipheral and peripheral zinc metallophthalocyanine compounds substituted with coumarin containing ester groups were synthesized, their chemical structures were characterized, and their aggregation behaviours were investigated. Their antioxidant properties were investigated using 1,1-diphenyl-2-picrylhydrazine (DPPH). The antioxidant activity studies revealed that the peripheral substituted phthalocyanine compound had higher antioxidant activity than the nonperipheral phthalocyanine compound. Their thermal analysis were investigated using thermal gravimetric analysis. Thermal analysis studies concluded that the synthesized zinc metal phthalocyanine compounds are thermally stable compounds.

This study presented a developed magnetic dispersive micro solid phase extraction method for the preconcentration and extraction of Co(II), Ni(II), and Cd(II) ions in aqueous samples. The method employed an effective magnetic adsorbent synthesized using cobalamin (vitamin B12) as the key component, combining the benefits of magnetic separation with cobalamin’s strong affinity for heavy metal ions. The adsorbed analytes were then eluted using diluted nitric acid solution and the analytes were quantified via flame atomic absorption spectrometry. Several parameters that influence the efficiency of the extraction process, including adsorbent amount, complexing agent amount, pH, salt addition, vortexing time, and desorption conditions were studied and optimized. Under optimized conditions, the method achieved linear ranges of 0.5–150 µg L^− 1 for Co(II) and Ni(II), and 0.1–150 µg L^− 1 for Cd(II). The limits of detection were 0.20, 0.18, and 0.04 µg L^− 1 for Co(II), Ni(II), and Cd(II) ions, respectively. The developed method demonstrated good repeatability, with relative standard deviations less than 5%. The method was effectively utilized to analyze real water and fruit juice samples, yielding good recoveries and precision. This approach offered a straightforward, sensitive, and eco-friendly alternative for determining heavy metals in complex matrices, with potential applications in environmental monitoring and food safety.

This study evaluates the effectiveness of five machine learning classifiers—Bayes Network, Logistic Model Tree, Decision Trees, Random Forest, and Sequential Minimal Optimization–Support Vector Machine (SMO-SVM)—for classifying biochemical oxygen demand (BOD) levels in wastewater. The models were trained using features extracted from visible and near-infrared (VIS–NIR) hyperspectral data. Prior to classification, robust principal component analysis (rPCA) was applied for dimensionality reduction. Six principal component scores, which collectively accounted for 99% of the explained variance, served as the input features for the algorithms. By comparing the models using the sum of ranking differences (SRD), we aim to identify the most accurate approach for classifying wastewater based on BOD values. After comparing the results of the output models with preprocessed data, the performance on wastewater samples was deemed acceptable. The Logistic Model Tree (LMT) and Random Forest (RF) models (SRD = 0) delivered the best performance, with overall classification accuracy (CCI) of 95.3% and 96.51%, respectively. Both models also exhibited excellent ROC areas (0.99), low error rates (MAE: 0.04 for LMT, 0.09 for RF), and strong balance between TP and FP rates, confirming their superior reliability. Additionally, BN, SMO–SVM, and DT, with SRD = 4, 8, and 12 respectively, and a probability of randomness falling between 3.32 × 10⁻⁶% and 9.12 × 10⁻⁵%, exhibit acceptable performance. These results underscore the potential of machine learning as a valuable tool for classifying BOD in wastewater management.

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A novel naphthalene chalcone type organogel was synthesized and the molecular structure assembly was investigated. The stable organogel could be formed in acetone, n-heptane and DMSO. The xerogels were prepared by freeze-drying organogels from acetone, n-heptane and DMSO, and their structures were investigated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The SEM images of xerogels showed the presence of a sheet-like and spatial network of gelator networks. Concentration-dependent absorbance and emission studies of the compounds indicate that the driving forces of the self-aggregation and gelator formation processes are confirmed.

The copper molybdate nanoparticles have proven promising as a potential electrocatalyst for the hydrogen evolution reaction (HER) as well as the oxygen evolution reaction (OER). There is currently a dearth of study on the catalytic potential of tungsten oxides in the oxygen evolution process. In this study, we introduce a new superaerophobic electrode with a Cu₃Mo₂O₉/CdS nanoarray structure. These unique electrode catalyses the HER in addition to OER shows remarkable stability and efficient functioning in alkaline conditions. In contrast to the conventional reversible hydrogen electrode, the Cu₃Mo₂O₉/CdS nanoarray configuration greatly enhances the electrode’s ability to discharge hydrogen gas. This leads to a remarkable current density of 10 mA cm^− 2 at an initial potential of about 241 mV, which corresponds to a 252 mV overpotential following HER. Furthermore, the Tafel slope of this Cu₃Mo₂O₉/CdS nanocomposite is 39.71 mVdec^− 1. Conversely, the Cu₃Mo₂O₉/CdS nanoarray has a significant OER activity because of its 241 mV overpotential and 1.42 mV onsets potential. Additionally, the resulting material exhibits Tafel slopes of around 39.71, 48.67, and 176.4 mVdec^− 1, which are used to validate the reaction mechanism using a current density of 10 mA cm^− 2. This suggests that the catalyst might be a non-noble metal that exhibits potential for water splitting when combined with 1 M KOH. Therefore, this study’s utilization of Cu₃Mo₂O₉/CdS nanoarray is a novel strategy to creating OER electrocatalysts in addition to other parts that comprise energy conversion, storage, and delivery systems.

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The role of biosensor technology to mimic the human senses is gaining popularity rapidly. The Nafion 117 can be used as one of the biosensors due to its biocompatibility and actuation property. The electrochemical analysis involving cyclic voltammetry and water-retaining ability through the water-holding capacity and ion exchange property has been studied for the same purpose. The high cost and operational complexity of gas sensors have led to the development of alternative materials. The ionic polymer metal composites, due to their unique sensing and biocompatibility properties, are evolving as important alternatives for the sensor materials. Nafion 117 is a type of IPMC, and the cost of Nafion 117-based E-sensors is relatively cheaper compared to other materials. The critical property of Nafion 117 for overall sensory performance coated with the neutral material makes it suitable to be used as a chemical sensor material for the investigation of food quality. These Nafion-117-based chemical sensors use the pattern-based recognition system for the same. With this pattern-based quality system, the sensors are used to match the pre-stored patterns with the desired responses. The high-resolution cameras and monitors were used to display the output responses. The low cost and low actuation energy of Nafion 117 make it suitable for the sensor technology. In addition to these properties, Nafion 117 also exhibits all kinds of strain mechanisms on application of applied voltage. This paper investigated the role of Nafion 117-based chemical sensors in the determination of food quality by the use of E-nose and E-tongue in the food industry. In this study, the investigation of Nafion 117 has been done through electrochemical analysis for E-sensors in the food industry.

Dopamine, a canonical neurotransmitter, is essential for regulating motor, cognitive, and emotional functions, and its dysregulation leads to an array of neuropsychiatric and neurodegenerative disorders such as Parkinson’s disease, schizophrenia, depression, and attention-deficit hyperactivity disorder. Given growing concerns about the possible influence of environmental pollutants on dopaminergic signaling, this study aimed to investigate whether bisphenol compounds and pyrethroid-derived metabolites, such as 3-phenoxybenzoic acid, could interact with dopamine receptors. To address this, molecular docking simulations were conducted for above-mentioned ligands while considering dopamine as a reference ligand. Structural models of dopamine receptors were prepared and subjected to molecular docking with PyRx software. The selected pollutants exhibited binding affinities ranging from − 7.6 to − 7.0 kcal/mol, compared with – 6.2 kcal/mol for dopamine. These computational findings suggest that the pollutants may show stronger binding affinities to dopamine receptors than dopamine itself, raising the possibility of competitive interactions at receptor binding sites. While these results provide preliminary in silico evidence, they remain predictive and require additional validation through experimental and in vivo studies to better clarify the potential neurotoxic implications of these pollutants.