Iranian Journal of Chemistry and Chemical Engineering
Volume:44 Issue: 5, May 2025

This experimental investigation first employed the laser ablation technique to synthesize zinc zeolite imidazolate framework (Zn-ZIF) as a subset of metal-organic frameworks (MOF) in a liquid environment. This experimental investigation irradiated a laser on a zinc metal target in a methanol (MeOH) solution containing 2-methylimidazole ligand to synthesize zeolitic imidazolate framework-8 (ZIF-8) nanostructures. Three samples of Zn-ZIF nanostructures were produced in three ligand concentrations. The antibacterial activity was evaluated by Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) of gram-negative and gram-positive bacteria, respectively. Based on the results, laser ablation is a rapid technique for the preparation of ZIF-8 nanostructures. With increasing ligand concentration, the number of nanostructures produced in the ablation liquid environment increased.

Corrosion mitigation remains a critical challenge in industries utilizing aluminium (Al), particularly in acidic environments where material degradation can compromise structural integrity and operational efficiency. The effectiveness of Polyvinyl Alcohol (PVA) as a corrosion inhibitor for Al in 1 M sulfuric acid (H₂SO₄) was evaluated through weight loss and polarization techniques. The study was conducted at three different temperatures (25 °C, 35 °C, and 45 °C) and varying concentrations of PVA (300, 450, and 600 ppm) to assess its inhibition efficiency. Results indicated that the inhibition efficiency increased with higher PVA concentrations, achieving maximum values of 90%, 88%, and 86% at 600 ppm for the respective temperatures. However, inhibition efficiency was found to decrease slightly with increasing temperature. Polarization studies revealed that PVA acted as a mixed-type inhibitor, reducing both anodic and cathodic currents. The maximum inhibition efficiency at 25°C was 91% as determined by polarization, with a corresponding reduction in corrosion current from 264.12 mA/cm² (blank) to 23.08 mA/cm² at 600 ppm. The study demonstrates the potential of PVA as an eco-friendly corrosion inhibitor for Al in acidic environments, as its performance is significantly influenced by both temperature and concentration.

This study investigates the removal of diazinon from aqueous solutions using rice bran as a bio-adsorbent in both its raw and modified forms within a discontinuous adsorption system. Key parameters influencing surface adsorption were systematically examined, including adsorbent pretreatment, particle size, concentration, temperature, and pH. The experimental design utilized the Taguchi method to assess the effects of these parameters at four distinct levels. The findings revealed that the optimal conditions for maximum adsorption—achieving an efficiency of 80.42%—occurred with rice bran pretreated with 0.5 M sodium hydroxide, an adsorbent concentration of 4 g/L, a pH of 5, a particle size of 40 mesh, and a temperature of 30 °C. Isotherm analyses indicated that the Langmuir isotherm model best represented the experimental data, while kinetic studies suggested that the pseudo-second-order model provided the most accurate fit. The thermodynamic analysis demonstrated that the adsorption process is spontaneous (ΔG°ads < 0) and exothermic (ΔH°ads< 0). Additionally, scanning electron microscopy (SEM) was employed to examine the structural characteristics of the adsorbent surface before and after treatment.

According to the radioactivity criteria, about 97% of radioactive waste is categorized as Low and Intermediate Level Waste (LLW or ILW). Such waste has been disposed of in near-surface repositories for many years all over the world. Surveillance programs, such as monitoring, are important elements in ensuring the safety of operation and closure of near-surface waste disposal facilities. During construction, operation, and closure, these safety parameters are assured by active control measures exercised at the repository and the quality management process. This paper primarily aims to examine the pre-operational radiomonitoring program for Iran's near-surface disposal site in Anarak. The goal is to measure baseline values of natural radioactive materials resulting from atmospheric deposition in the repository's surrounding environment. as well as to track their seasonal variations in water, plants, and the atmosphere. The analysis of soil and sediment radiomonitoring will be covered in the next study. In such a manner, after the site selection process, a comprehensive plan is designed for the Anarak waste repository, including sampling methods, sampling frequency, sampling stations, and key radionuclides. After the nuclear facility construction, the baseline radiation values are recorded to assess any changes in the zone's radioactive levels for the long-term operational and post-closure safety assessment programs, which is crucial to determine when and where radioactive contamination becomes hazardous for industrial workers and the public. The results showed, the beta particle radioactivity concentration in nine samples of water is measured from 0.12 to 0.94, and this range varies from 0.14 to 0.90 for alpha particles in the same samples. Moreover, the 40K ،137Cs ,and 232Th concentrations in nine samples are less than the Minimum Detectable Activity (MDA). Finally, all measured values were compared and analyzed with national and international guidelines to record as a baseline value for the Anarak disposal facility and the surrounding environment.

Xenon (Xe) gas adsorption in Metal-Organic Frameworks (MOFs) is a critical area for noble gas separation due to Xe's scarcity and high market value. Despite its importance, previous studies have largely overlooked the role of diverse Machine Learning (ML) models in predicting gas adsorption behavior under varying pressures. This study aims to fill this gap by developing a comprehensive database of hypothetical MOFs and applying advanced ML frameworks to predict Xe adsorption. Key structural descriptors—Void Fraction, Gravimetric Surface Area, Volumetric Surface Area, Pore Limiting Diameter, and Large Cavity Diameter—were integrated alongside adsorption pressure to enhance predictive accuracy. We trained and evaluated multiple ML models, including Ensemble Learning, Exponential Gaussian Process Regression, Fine Gaussian Support Vector Machines, and Bilayered Neural Networks, based on metrics such as RMSE (0.937 for EGPR), R² (0.83 for EGPR), and processing speed (up to 58,000 observations per second for FGSVM). Our screening identified four optimal MOFs—hMOF-30258, hMOF-30132, hMOF-5001015, and hMOF-30001—with superior Xe adsorption capabilities, featuring pcu and sql topologies that offer high surface area and porosity. These results highlight the potential of ML-driven approaches to revolutionize MOF design, paving the way for efficient noble gas separation technologies.

Biochips and microchemical systems utilize micromixers to mix various reactants in microscale environments effectively. The innovative microfluidic device is described. This study aims to enhance the Mixing Index (MI) by changing geometric parameters and inlet velocity. This micromixer consists of two unbalanced U-shaped channels connected by a chevron obstacle within a hexagonal mixing chamber, which promotes efficient fluid interaction. Numerical simulations are performed using the finite element-based COMSOL Multiphysics software, and the results are used to optimize the micromixer performance using Response Surface Methodology (RSM). It is found that the inlet velocity greatly affects the MI and pressure drop (Δp), whereas the angle between the inlets does not affect either of these parameters. For instance, MI increases from 80.85% to 94.52% when Uin is raised from 0.01 m/s to 1 m/s. In general, the suggested micromixer exhibits an MI higher than 80% for all ranges of the considered independent variables. Furthermore, two correlations for MI and Δp are provided by the RSM that can be used to maximize this micromixer's performance.

Microchannel Heat Sinks (MCHSs) are extensively used in various fields, including electronics cooling, aerospace, and energy systems, due to their compact size, high thermal efficiency, and ability to effectively manage thermal performance in devices with high heat flux densities. This study introduces novel MCHS configurations to assess the impact of geometric parameters on Performance Evaluation Criteria (PEC). Additionally, the study investigates the use of a Water-Based Ternary Hybrid Nanofluid (W-THNF). The findings indicate that among all MCHS configurations, the base case shows the lowest Surface Temperature Uniformity (STU), while Case 4 achieves the highest STU. Furthermore, Case 4 consistently exhibits a higher Heat Transfer Coefficient (HTC) compared to other cases across all Reynolds numbers (Re). It is revealed that Case 1 exhibits the highest PEC among all configurations. For instance, at Re = 600, Case 1 shows a PEC increment of 7.02%, 13.15%, 18.36%, and 22.72% compared to Re = 800, 1000, 1200, and 1400, respectively. At all amounts of Re, the convective HTC is enhanced with the volume fraction (φ) of W-THNF. For example, the convective HTC in Case 1 is enhanced by 5.62%, 10.76%, 16.92%, and 24.05% for φ = 0.03, 0.05, 0.07, and 0.09, respectively, compared to pure water when Re = 600.

The lack of fresh water is one of the most common issues people face. Employing a Solar Still (SS) to transform saltwater into drinkable water can reduce the shortage of drinking water. Still, the lower yield of SS remains a drawback. The use of energy storage materials can significantly improve an SS water output. Four separate experiments were carried out:  SS, SS with tar-coated blue metal stones at a 1 cm water level (SS+TC-BMS at 1 cm WL), SS+TC-BMS at 2 cm WL, and SS+TC-BMS at 3 cm WL. Additionally, the thermal and exergy efficiency are assessed. The distilled water produced from SS+TC-BMS at 1 cm WL was 3.82 Kg, and SS+TC-BMS at 2 cm WL was 3.29 Kg, representing an increase of 59.4% and 37.2%, respectively, than the SS. The yield during the daytime (7 AM- 6 PM) from SS, SS+TC-BMS at 1 cm WL, SS+TC-BMS at 2 cm WL, and SS+TC-BMS at 3 cm WL were 2.32, 3.49, 2.91, and 2.15 kg, respectively. Similarly, the generation of distilled water at night (7 PM to 11 PM) was 0.08, 0.33, 0.38, and 0.22 for SS, SS+TC-BMS at 1 cm WL, SS+TC-BMS at 2 cm WL, and SS+TC-BMS at 3 cm WL, respectively. Additionally, the SS+TC-BMS's diurnal thermal and exergy efficiency of the SS+TC-BMS at 1 cm WL was 43.3%, and 2.2%, respectively. In contrast, SS's diurnal thermal and exergy efficiency were 30.9% and 1.26%, respectively.

The spiral plate heat exchanger is designed as self-cleaning equipment with a low propensity for fouling. It is easily accessible for inspection and mechanical cleaning and occupies minimal space. Numerous studies have explored the use of various nanoparticles to enhance the performance of heat exchangers. This study examines the influence of Al2O3 nanoparticles' size, shape, and temperature on the thermodynamic parameters of a spiral plate heat exchanger, including the Nusselt number, overall heat transfer coefficient, Stanton, and Reynolds numbers. The findings indicate that an increase in the nanoparticle's diameter by 80 nm leads to approximately a 65% increase in the Nusselt number. It was also found that in the case of platelet-shaped nanoparticles, the Prandtl number increases by about 2.24 times as the volume fraction rises from 1% to 5%.  Additionally, at a specific volume fraction, the highest Prandtl number is recorded for the platelet-shaped nanoparticle, while the lowest is noted for the spherical nanoparticle shape. The results demonstrate that the overall heat transfer coefficient increases as the nanoparticle temperature rises.

The growing need for sustainable energy solutions has driven advancements in waste heat recovery and renewable energy technologies. This research explores the efficiency of two hybrid configurations that merge Kalina cycles (KCS-11 and KCS-34) with a dual-ejector organic flash cycle, utilizing geothermal energy as the primary heat source. A thorough thermodynamic study is conducted, focusing on system performance, the effect of important factors like geothermal input temperature, ammonia mass fraction, and turbine inlet pressure. The KCS-34-DE-OFC system demonstrated superior performance compared to the KCS-11-DE-OFC system, achieving a peak net power output of 584.8 kW along with maximum thermal and exergy efficiencies of 16.55% and 57.56%, respectively. This study demonstrates the significant potential of dual-ejector integration and advanced cycle configurations for efficient geothermal energy utilization, offering a sustainable approach to meeting global energy demands.

The growing threat of climate change necessitates urgent action to mitigate carbon emissions, particularly in major emitters like China, which has implemented significant carbon reduction policies, including a carbon emission trading policy. Simultaneously, the fast-paced expansion of the digital economy has prompted businesses to undergo digital transformation, serving as an important catalyst for economic growth, although its influence on energy consumption is still unclear. This study investigates whether the carbon emission trading policy influences energy consumption by facilitating this digital transformation. Analyzing data from 283 Chinese cities between 2006 and 2019, the research employs a multi-time-point difference-in-difference model to assess the policy's direct effects on energy consumption, with digital transformation measured through annual report analysis. The results demonstrate that the CO2 trading policy substantially increases urban energy consumption (UEC), with digital transformation playing a partial mediating role in this relationship. The study's novel use of a multi-time-point DID model enables a nuanced analysis of the heterogeneous impacts of the staggered carbon trading policy rollout on energy consumption across Chinese cities, providing robust insights to inform effective climate policy design. Furthermore, the regulatory effect analysis indicates that the higher the degree of digital transformation, the more significant the promotion effect of the carbon emission trading policy on energy consumption, suggesting that digital transformation amplifies the policy's impact. Notable regional disparities in science and technology, internet availability, and government involvement result in different effects of carbon trading policies on energy consumption. The study shows the important role of digital economy transformation in carbon emission policies and proposes that regional characteristics should be fully considered when formulating policies to achieve more effective carbon emission reduction and energy structure optimization.

Global warming presents a significant challenge, largely driven by carbon dioxide emissions from conventional power generation. Renewable energy technologies, particularly biomass-integrated systems, provide a promising pathway to mitigate these emissions sustainably. This study conducts a comprehensive energy, exergy, and environmental assessment of a tri-generation biomass system incorporating gasification, anaerobic digestion, and a Solid Oxide Fuel Cell (SOFC). Two configurations are examined: one utilizing syngas from gasification and the other biogas from anaerobic digestion. The results show that the biogas-based system achieves a superior exergy efficiency of 44.22%, compared to 39.18% for the gasification-based system, while also reducing CO2 emissions by 0.072 tons/MWh. Additionally, optimizing the fuel utilization factor (0.76 for the digester-SOFC and 0.78 for the gasifier-SOFC) significantly enhances system performance. These findings underscore the environmental benefits of biogas in tri-generation systems and provide valuable insights into optimizing biomass-based energy solutions.

The reliability of fault diagnosis and the stability of electrical grids demand accurate analysis of key chemical gases in power transformer oil. DGA, which quantitatively measures concentrations of critical chemical gases, is still a cornerstone technique in the field, but is infamous for its interpretative complexity: the correlation between gas levels and specific fault types is too complex. The proposed methodology in this study attempts to integrate the strengths of traditional methods of DGA interpretation along with the power of a machine learning model, specifically a Random Forest algorithm. The process comprises the preprocessing of DGA data to extract meaningful chemical features from them further developing the model using machine learning to classify the different kinds of faults based upon those chemical features. This approach has been validated on multiple scenarios for the data coming from DGA transformer faults after a lot of testing. Results show that this method delivered an average accuracy of 95.86% for three types of faults and 93.67% for the same types of faults with varying conditions. For six types of faults, the delivery was placed at an average accuracy and consistency of 88.85% and 87.47%, respectively. This approach significantly shows improved performance in the traditional methods of diagnostics while promising much more accurate fault detection. In addition to enhanced diagnostic accuracy, it supports proactive, hence preventive, maintenance strategies, resulting in improved system efficiency and reduced downtime. The paper details a technique that combines chemical data analysis with machine learning, from which distinct solutions can be conceived to address the complex challenges facing industries.

Urea in combination with glycerol was applied in the process intensification of Urea Glycerol Microwave-Assisted Extraction (UG-MAE) of the essential oil of Cymbopogon citratus. The objective of this research was to evaluate the effect of urea-glycerol molar ratio and leaf length on the UG-MAE of the essential oil of Cymbopogon citratus. This work also proposed and validated a kinetics model based on the diffusion-control assumption. The UG-MAE for Cymbopogon citratus was carried out in a microwave extractor by altering the molar ratio of urea and glycerol and the leaf length. The research findings indicated that urea-glycerol functions effectively as a deep eutectic solvent for extracting the essential oil of C. citratus. It was observed that the solubilization rate of essential oil was greater in shorter leaves of C. citratus compared to longer leaves. In comparison, the solubilization rate for an equal molar ratio of urea-glycerol was lower than that with a higher ratio of urea to glycerol. Optimal conditions for the UG-MAE process were identified and reported in this study. Gas chromatography analysis indicated that Z-citral, E-citral, and trans-caryophyllene are the three primary components present  in the essential oil of Cymbopogon citratus obtained through the UG-MAE process. The proposed diffusion-controlled slab particle model was validated, demonstrating a good fit for the experimental data. The kinetics parameters obtained for the UG-MAE of Cymbopogon citratus included the Henry constant, which varied between 0.24 and 0.30, and the effective diffusivity, which ranged from 5.08.E-07 to 3.93.E-06 cm2/s. The concentration profile of the essential oil, expressed as the ratio of oil concentration to the initial oil concentration concerning axial position and time in the solid particle, provides greater insight into the UG-MAE behavior related to Cymbopogon citratus.

A novel methodology has been established, enhancing the effectiveness of extraction methods for monoammonium glycyrrhizate derived from licorice root powder through hydrothermal technology, utilizing the Response Surface Methodology (RSM). By varying extraction solvents, techniques, durations (ranging from 15 to 45 minutes), and temperatures (between 150 and 200 °C), optimal extraction conditions were identified. The study measured 47.15 mg/g of monoammonium glycyrrhizate, while also assessing the impact of operating pressure (from 0.5 to 2.5 MPa) on the response variables of monoammonium glycyrrhizate using High-Performance Liquid Chromatography (HPLC). The findings revealed that, at a fixed operating pressure,  a significant increase in extraction temperature corresponded with an enhanced extraction rate of monoammonium glycyrrhizate from licorice root. Additionally, the combined influence of extraction temperature and duration, as well as operating pressure and time, exhibited a saddle curve pattern, indicating that the maximum extraction rate for these bioactive compounds occurred at 30 minutes. The optimal parameters for extracting bioactive compounds via hydrothermal technology were determined to be 200 °C, 30.29 minutes, and 0.5 MPa, yielding a desirability score of 0.75, with monoammonium glycyrrhizate concentrations measured at 47.15 mg/g, respectively. This investigation underscores the potential of hydrothermal technology and environmentally benign Methods for the effective extraction of bioactive compounds from licorice root, presenting significant opportunities for Utilization in the food sector, sectors of medicine, and pharmaceutical.

Numerous natural medicines derived from Chinese herbs have been discovered in recent years to have anti-cancer potential both in vitro and in silico. These chemicals have been shown to restrict angiogenesis, limit proliferation, induce apoptosis, delay metastasis, and improve chemotherapy. Flos Magnoliae is the source of aschantin, a tetrahydrofurofuran lignan containing a 1,3-benzodioxole group that has biological properties. IC50 values of aschantin for ovarian cell lines (ES-2, NIH-OVCAR-3, Hs832.Tc, UACC-1598, TOV-21G, UWB1.289) were 43.78± 3.06, 54.62 ± 4.17, 57.22± 6.13, 35.50 ± 5.65, 28.34 ± 2.53, and 39.42 ± 4.70 µM, respectively. For HMG-CoA reductase enzyme, IC50 values of natural compound and standard were 26.08±3.38 and 83.77±7.60 µM. The biological effects of Aschantin on HMG-CoA reductase were evaluated through a series of methodologies, including molecular modeling studies, MM/GBSA calculations, and Molecular Dynamics (MD) simulations. Additionally, the anti-cancer properties of this molecule were tested against various ovarian cancer cell lines, specifically ES-2, NIH-OVCAR-3, Hs832.Tc, UACC-1598, TOV-21G, and UWB1.289. The chemical interactions of Aschantin with several surface receptor proteins, such as CD44, folate receptor, Formyl Peptide Receptor–Like 1, EGFR, estrogen receptors, and M2 muscarinic receptor, were explored using computational techniques. The findings revealed significant interactions at the atomic level, indicating that the compound established strong connections with both the enzymes and receptors. Aschantin demonstrates the potential to inhibit the activity of this enzyme and suppress the growth of cancerous cells.

Lung Cancer (LC), a prominent cause of cancer-related mortality globally, necessitates innovative therapies with strong pharmacokinetics. This study evaluated the anticancer potential of sakuranin in A549 LC cells and a xenograft model, focusing on cytotoxicity, pro-oxidative, apoptotic, anti-metastatic, and cell cycle arrest effects, alongside molecular interactions with PI3K/AKT/mTOR pathway proteins. The physicochemical properties and Absorption, Distribution, Metabolism, Excretion (ADME)/toxicity profile were analyzed via SwissADME and ProTox-3.0. Cytotoxicity was assessed via MTT assay, Reactive-Oxygen-Species (ROS) using DiChloro-dihydro-Fluorescein-DiAcetate (DCFH-DA) fluorescence, Mitochondrial Membrane Potential (MMP) via Rhodamine (Rh)-123 staining, apoptosis via 4',6-diamidino-2-phenylindole (DAPI)-based nuclear morphology, and cell cycle arrest via flow cytometry. Anti-migratory/invasive effects were tested using transwell assays. In vivo efficacy was observed in a xenograft model, and molecular docking evaluated binding interactions to PI3K/AKT/mTOR proteins. Sakuranin complied with Lipinski’s rules, demonstrating favorable solubility and bioavailability. It exhibited dose-dependent cytotoxicity in A549 cells (IC₅₀: 74.22 µg/mL; 82% viability reduction at the highest dose). ROS levels tripled, while MMP depolarization reduced fluorescence intensity by 59%. Apoptotic nuclear changes were observed, and cell cycle analysis revealed 70% G2/M phase arrest. Migration and invasion decreased by 70% and 65%, respectively. In vivo, a 200 mg/kg dose resulted in a 90% drop in tumor volume and a 72% drop in tumor weight. Molecular docking studies confirmed strong interactions of sakuranin with PI3K (-9.2 kcal/mol), AKT (-10.5 kcal/mol), mTOR (-8.7 kcal/mol), and ERK (-8.1 kcal/mol), suggesting its role in modulating the PI3K/AKT/mTOR signaling cascade. Sakuranin demonstrates potent cytotoxic, pro-apoptotic, and anti-metastatic effects in LC models. Its interaction with key signaling proteins underscores its therapeutic potential. These findings advocate further exploration of sakuranin as a promising LC treatment candidate targeting the PI3K/AKT/mTOR axis.

Several new natural compounds were investigated as ALK inhibitor compounds. Bavachin, bavachinin, maesopsin, garcinoic acid, and bilobol Compounds, which have active groups, and also can inhibit ALK enzymatic tests (IC50: 0.018 ± 0.007, 1.830 ± 0.012, 9.141 ± 0.301, 0.048 ± 0.003, and 0.970 ± 0.030 μM, respectively). Bavachin, Garcinoic acid, and Bilobol were identified as the best inhibitors, and then the antiproliferative activity of these compounds was studied. Additionally, the studies continued with bilobol, Bilobol against various receptor tyrosine Kinases, various ALK Mutants, CYP450 Enzymes, and hERG studies were performed, and comparisons were made with standards. The investigation of the biological activities of certain natural compounds against a variety of enzymes was conducted using molecular modeling and molecular simulation. Computational techniques were utilized to assess the effectiveness of the natural compounds in inhibiting ALK and specific mutations of this kinase. Additionally, the binding affinity of these compounds to various receptor tyrosine kinases, including EGFR, c-Met, VEGFR2, c-Src, IGF1R, and JAK2, was analyzed. The results emphasized the interactions between atoms, demonstrating that the compounds formed strong connections with the proteins. These natural compounds exhibit the potential to suppress enzyme activity and hinder the proliferation of cancer cells. Natural products have long been the sole source of viable lead compounds for medicinal chemists. They have also given clinical practice access to a large number of potential medication candidates for the therapy of a variety of conditions.