Search Results (224)

This review outlines a comprehensive methodology for the research and development of heterogeneous catalytic technologies (R&D_HeCaTe). Emphasis is placed on the fundamental interactions between reactants, solvents, and heterogeneous catalysts—specifically the roles of catalytic centers and support materials (e.g., functional groups) in modulating activation energies and stabilizing catalytic functionality. Particular attention is given to catalyst deactivation mechanisms and potential regeneration strategies. The application of molecular modeling and chemical engineering analyses, including reaction kinetics, thermal effects, and mass and heat transport phenomena, is identified as essential for R&D_HeCaTe. Reactor configuration is discussed in relation to key physicochemical parameters such as molecular diffusivity, reaction exothermicity, operating temperature and pressure, and the phase and “aggressiveness” of the reaction system. Suitable reactor types—such as suspension reactors, fixed-bed reactors, and flow microreactors—are evaluated accordingly. Economic and environmental considerations are also addressed, with a focus on the complexity of reactions, selectivity versus conversion trade-offs, catalyst disposal, and separation challenges. To illustrate the breadth and applicability of the proposed framework, representative industrial processes are discussed, including ammonia synthesis, fluid catalytic cracking, methanol production, alkyl tert-butyl ethers, and aniline. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R² values of up to 0.99, which carries significant implications for track design and maintenance. Full article

Background: Sodium–glucose cotransporter 2 (SGLT2) inhibitors have transformed type 2 diabetes mellitus (T2DM) management by promoting glucosuria, lowering glycated hemoglobin (HbA1c), blood pressure, and weight; however, their use is limited by genitourinary infections and ketoacidosis. Phytocannabinoids—bioactive compounds from Cannabis sativa—exhibit multi-target pharmacology, including interactions with cannabinoid receptors, Peroxisome Proliferator-Activated Receptors (PPARs), Transient Receptor Potential (TRP) channels, and potentially SGLT2. Objective: To evaluate the potential of phytocannabinoids as novel modulators of renal glucose reabsorption via SGLT2 and to compare their efficacy, safety, and pharmacological profiles with synthetic SGLT2 inhibitors. Methods: We performed a narrative review encompassing the following: (1) the molecular and physiological roles of SGLT2; (2) chemical classification, natural sources, and pharmacokinetics/pharmacodynamics of major phytocannabinoids (Δ⁹-Tetrahydrocannabinol or Δ⁹-THC, Cannabidiol or CBD, Cannabigerol or CBG, Cannabichromene or CBC, Tetrahydrocannabivarin or THCV, and β-caryophyllene); (3) in silico docking and drug-likeness assessments; (4) in vitro assays of receptor binding, TRP channel modulation, and glucose transport; (5) in vivo rodent models evaluating glycemic control, weight change, and organ protection; (6) pilot clinical studies of THCV and case reports of CBD/BCP; (7) comparative analysis with established synthetic inhibitors. Results: In silico studies identify high-affinity binding of several phytocannabinoids within the SGLT2 substrate pocket. In vitro, CBG and THCV modulate SGLT2-related pathways indirectly via TRP channels and CB receptors; direct IC₅₀ values for SGLT2 remain to be determined. In vivo, THCV and CBD demonstrate glucose-lowering, insulin-sensitizing, weight-reducing, anti-inflammatory, and organ-protective effects. Pilot clinical data (n = 62) show that THCV decreases fasting glucose, enhances β-cell function, and lacks psychoactive side effects. Compared to synthetic inhibitors, phytocannabinoids offer pleiotropic benefits but face challenges of low oral bioavailability, polypharmacology, inter-individual variability, and limited large-scale trials. Discussion: While preclinical and early clinical data highlight phytocannabinoids’ potential in SGLT2 modulation and broader metabolic improvement, their translation is impeded by significant challenges. These include low oral bioavailability, inconsistent pharmacokinetic profiles, and the absence of standardized formulations, necessitating advanced delivery system development. Furthermore, the inherent polypharmacology of these compounds, while beneficial, demands comprehensive safety assessments for potential off-target effects and drug interactions. The scarcity of large-scale, well-controlled clinical trials and the need for clear regulatory frameworks remain critical hurdles. Addressing these aspects is paramount to fully realize the therapeutic utility of phytocannabinoids as a comprehensive approach to T2DM management. Conclusion: Phytocannabinoids represent promising multi-target agents for T2DM through potential SGLT2 modulation and complementary metabolic effects. Future work should focus on pharmacokinetic optimization, precise quantification of SGLT2 inhibition, and robust clinical trials to establish efficacy and safety profiles relative to synthetic inhibitors. Full article

Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO₂, O₃, PM₁₀, and PM_2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM₁₀ forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. Full article

Transdermal drug delivery offers a non-invasive route for the systemic and localized administration of therapeutics; however, the skin’s barrier function limits its efficiency. This study investigates the application of various electromagnetic field (EMF) configurations to enhance the transdermal delivery of salicylic acid, a model compound with moderate lipophilicity and ionizability. Samples were exposed to pulsed, oscillating, static, and rotating magnetic fields, and their effects on physicochemical properties, thermal stability, skin permeation, and accumulation were evaluated. Structural analyses (FTIR, XRD) and thermal assessments (TGA, DSC) confirmed that EMF exposure did not alter the chemical structure or stability of salicylic acid. In vitro transdermal studies using porcine skin and Franz diffusion cells revealed that pulsed magnetic fields—especially with a 5 s on/5 s off cycle—and rotating magnetic fields at 30–50 Hz significantly enhanced drug permeation compared to controls. In contrast, static fields of negative polarity increased skin retention, suggesting their potential for controlled, localized delivery. These findings demonstrate that EMFs can be used as tunable, non-destructive tools to modulate drug transport across the skin and support their integration into transdermal delivery systems aimed at optimizing therapeutic profiles. Full article

Varshavskii’s ‘Diffusion Theory’, less investigated due to its limited international visibility, can offer one of the simplest and, on the other hand, high-accuracy methods for evaluating the ignition delay of fossil fuel and biofuel droplets, including their blend. In this study, experimental pre-tests were conducted to determine pre-existing subject knowledge on stationary droplet combustion at ambient pressure and temperatures varying from 935 to 1010 K followed by simulation of droplet ignition times. The test fuels were mineral diesel (DF), RME and a 20% RME blend with DF. Simulations were performed for isobaric conditions. Using the detailed transport model and detailed chemical kinetics, the necessary rearrangements were made for the governing equations to meet the criteria for modern fuels (biodiesel, diesel, and blend). The influence of different physical parameters, such as droplet radius, or initial conditions, on the ignition delay time was investigated. The high sensitivity of the proposed methodology to experimental results was substantiated. Full article

Objectives: Ex vivo nasal mucosa models provide physiologically relevant platforms for evaluating nasal drug permeability; however, their application is often limited by high experimental variability and the absence of standardized methodologies. This study aimed to improve experimental design by addressing two major limitations: the confounding effects of mucosal thickness and the questionable reliability of fluorescein sodium (Flu-Na) as an integrity marker for chemically induced mucosal injury. Methods: Permeability experiments were conducted using porcine nasal tissues mounted in Franz diffusion cells, with melatonin and Flu-Na as model compounds. Tissues of varying thickness were collected from both intra- and inter-individual sources, and a numerical simulation-based method was employed to normalize apparent permeability coefficients (Papp) to a standardized mucosal thickness of 0.80 mm. The effects of thickness normalization and chemically induced damage were systematically evaluated. Results: Thickness normalization substantially reduced variability in melatonin Papp, particularly within same-animal comparisons, thereby improving statistical power and data reliability. In contrast, Flu-Na exhibited inconsistent correlations across different pigs and failed to reflect the expected increase in permeability following isopropyl alcohol (IPA)-induced epithelial damage. These results suggest that the relationship between epithelial injury and paracellular transport may be non-linear and not universally applicable under ex vivo conditions, limiting the suitability of Flu-Na as a standalone marker of mucosal integrity. Conclusions: The findings highlight the importance of integrating mucosal thickness correction into standardized experimental protocols and call for a critical reassessment of Flu-Na in nasal drug delivery research. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by synaptic dysfunction and neuronal loss due to the accumulation of amyloid-β peptides and tau proteins. In the pursuit of novel neuroprotective strategies, organotin(IV) compounds have garnered attention due to their unique chemical and biological properties. This study evaluates the inhibitory potential of two triphenyltin(IV) derivatives—(3-propan-1-ol)triphenyltin(IV) (Ph₃SnL₁) and (4-butan-1-ol)triphenyltin(IV) (Ph₃SnL₂)—in both free form and immobilized into mesoporous silica SBA-15~Cl, targeting acetylcholinesterase (AChE), a key enzyme involved in AD pathophysiology. The SBA-15~Cl|Ph₃SnL₂ nanostructures exhibited the most potent inhibitory activity against AChE (IC₅₀ = 0.58 μM), significantly outperforming the standard drug galantamine. Molecular docking, molecular dynamics simulations, and MM/GBSA and MM/PBSA analyses confirmed the stability and selectivity of interactions with AChE, primarily driven by hydrophobic interactions. Compound transport was also simulated using a multi-scale 3D mouse brain model to evaluate brain tissue distribution and blood–brain barrier permeability. The results highlight the strong potential of SBA-15-loaded organotin(IV) compounds as biocompatible neuroprotective agents for novel treatments of neurodegenerative diseases. Full article

Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM_2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM_2.5 are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM_2.5 are available through networks of monitors; however, these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM_2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here, we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods, and evaluation via cross-validation that is implemented in the software. We also provide a case study of estimating PM_2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. Full article

Phosphonate-based fungicides are believed to control fungal diseases while also supplying nutrients to plants. However, opinions differ on whether they truly serve as nutrients for plants, and the residues of their transformation products have not yet been thoroughly evaluated or mathematically characterized. To address this gap, this study analyzed data from a two-factorial experiment investigating the effects of Agrifos 400 (potassium phosphonate) application. The experiment involved two soil types: red basalt soil and an organically enriched soil. Three-month-old pepper plants (Piper nigrum L.) were treated with Agrifos at application intervals of 10 and 20 days. The soils were inoculated with pathogenic Pythium spp., known to cause root rot diseases in plants. The soil chemical concentrations were analyzed every ten days, while plant growth parameters (height and leaf numbers) were recorded weekly. A mathematical model describing the fate of Agrifos transformation products was developed and parameterized using this experimental data. The results from the two-month experiment indicated that Agrifos did not enhance plant growth during this period. However, it led to a dramatic increase in soil phosphate (PO₄³⁻) levels, which could pose environmental risks. Despite this, the developed mathematical model demonstrated strong explanatory power, accurately capturing the observed data trends. Consequently, future research should consider integrating this model into broader biogeochemical cycle simulations, particularly those that incorporate chemical transport through soil water. Such integration would support more accurate predictions of the long-term environmental impacts of phosphonate-based products like Agrifos. Full article

The objective of this article is to conduct a review to analyze global trends in the use of air pollution models under the influence of climate variability (CV) over urban areas. Five scientific databases were used (2013–2024): Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The frequency of citations of the variables of interest in the selected scientific databases was analyzed by means of an index using quartiles (Q). The results showed a hierarchy in the use of models: regional climate models/RCMs (Q3) > statistical models/SMs (Q3) > chemical transport models/CTMs (Q4) > machine learning models/MLMs (Q4) > atmospheric dispersion models/ADMs (Q4). RCMs, such as WRF, were essential for generating high-resolution projections of air pollution, crucial for local impact assessments. SMs, such as GAM, excelled in modeling nonlinear relationships between air pollutants and climate variables. CTMs, such as WRF-Chem, simulated detailed atmospheric chemical processes vital for understanding pollutant formation and transport. MLMs, such as ANNs, improved the accuracy of predictions and uncovered complex patterns. ADMs, such as HYSPLIT, evaluated air pollutant dispersion, informing regulatory strategies. The most studied pollutants globally were O₃ (Q3) > PM (Q3) > VOCs (Q4) > NOx (Q4) > SO₂ (Q4), with models adapting to their specific characteristics. Temperature emerged as the dominant climate variable, followed by wind, precipitation, humidity, and solar radiation. There was a clear differentiation in the selection of models and variables between high- and low-income countries. CTMs predominated in high-income countries, driven by their ability to simulate complex physicochemical processes, while SMs were preferred in low-income countries, due to their simplicity and lower resource requirements. Temperature was the main climate variable, and precipitation stood out in low-income countries for its impact on PM removal. VOCs were the most studied pollutant in high-income countries, and NOx in low-income countries, reflecting priorities and technical capabilities. The coupling between regional atmospheric models and city-scale air quality models was vital; future efforts should emphasize intra-urban models for finer urban pollution resolution. This study highlights how national resources and priorities influence air pollution research over cities under the influence of CV. Full article

The electrocatalytic hydrogenation (ECH) of biomass-derived phenolic compounds is a promising approach to the production of value-added chemicals and biofuels in a sustainable way under moderate reaction conditions. This study provides a comprehensive comparison of electrochemical and thermochemical hydrogenation processes, highlighting their relative advantages in terms of energy efficiency, product selectivity, and environmental impact. Several electrocatalysts (Pt, Pd, Rh, Ru), membranes (Nafion, Fumasep, GO-based PEMs), and reactor configurations are tested for the selective conversion of model compounds such as phenol, guaiacol, furfural, and levulinic acid. The contributions made by the electrode material, electrolyte composition, membrane nature, and reaction conditions are critically evaluated in relation to Faradaic efficiency, conversion rates, and product selectivity. The enhancement in the performance achieved by a new catalyst architecture is emphasized, such as MOF-based systems and bimetallic/trimetallic catalysts. In addition, a demonstration of graphite-based membranes and membrane-separated slurry reactors (SSERs) is provided, for enhanced ion transport and reaction control. The results illustrate the potential of using ECH as a low-carbon, scalable, and tunable method for the upgrading of biomass. This study offers valuable insights and guidelines for the rational design of next-generation electrocatalytic systems toward green chemical synthesis and emphasizes promising perspectives for the strategic development of electrochemical technologies in the pathway of a sustainable energy economy. Full article

CAMx-LPiG (Comprehensive Air Quality Model with Extensions—Linear Plume in Grid) is an online hybrid model based on the Chemistry and Transport Model (CTM) CAMx, which includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work, the model is presented and evaluated against measured NO₂ concentrations for the city of Milan for the month of January 2017. The model can introduce road traffic-induced gradient in NO₂ concentration at sub-grid resolution. Moreover, CAMx-LPiG has been shown to reduce bias compared to CAMx stand-alone simulations. Full article

Soil–bentonite (SB) vertical cut-off walls are widely utilized to mitigate the transport of soil contaminants in groundwater. Evaluating their long-term service performance is crucial for ensuring environmental safety and effective pollution control. The evaluation model for the long-term service performance of contaminant cut-off walls considers key processes such as convection, diffusion, dispersion, and adsorption. These processes are closely linked to the physicochemical properties of the cut-off walls, which are influenced by the surrounding complex environment, ultimately impacting their long-term performance. This study delves into the long-term service performance of SB vertical cut-off walls. It focuses on the key factors that influence this performance and the measures that can enhance it. Moreover, it offers a detailed analysis of how the performance of seepage cut-off walls in soil–bentonite materials evolves under various environmental influences. These influences include chemical exposure, freeze–thaw cycles, and dry–wet cycles. Additionally, it outlines existing service performance evaluation methods and identifies their shortcomings. By leveraging the advantages of in situ testing methods, this paper proposes the establishment of a comprehensive evaluation system for the service performance of vertical cut-off walls based on in situ test parameters. The proposed evaluation system aims to provide a scientific assessment of the long-term service performance of SB vertical cut-off walls. Full article