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Synthesis of SnS2 modified to sulfated tin oxide by electrochemical method and VOC sensing properties
No full textThis study investigates the room-temperature gas-sensing performance of next-generation sensors fabricated by electrochemically transforming 2D SnS2 films into SO42−/SnO2 structures. The sensors were prepared on an interdigital transducer via the spin-coating method, followed by low-potential electrochemical oxidation in a sulfuric acid–methanol medium to form a 3D SO42−/SnO2 structure. Unlike conventional high-temperature oxidation or chemical etching methods that cause bulk degradation, this study employs a low-potential electrochemical oxidation–sulfation strategy to controllably convert the SnS2 surface into SO42−/SnO2 while preserving the nanostructure. While the Sn core structure remained intact, FTIR, EDX, and XPS analyses confirmed the successful surface sulfation and the formation of sulfate-related chemical states on the SnO2 surface. XRD analysis verified crystalline-level structural transformation, and SEM imaging revealed distinct surface morphology changes. The gas-sensing performance was systematically evaluated against VOC's vapors over a concentration range of 50–350 ppm, enabling a comprehensive assessment of sensitivity and selectivity. Results showed that the SnS2-based sensor exhibited high sensitivity to acetone, whereas the SO42−/SnO2 structure demonstrated nearly tenfold enhanced responsiveness to NH3 vapor. Sulfate functionalization introduced Lewis acidic surface sites, strengthening interactions with NH3 and enabling nA-level responses. Although increased humidity (30–90 % RH) reduced response amplitude, reliable NH3 sensing was maintained, with interference tests at 50 % RH confirming robust performance. Furthermore, stable and repeatable signals over 10 days demonstrated excellent durability. These results highlight electrochemical surface engineering as an effective strategy to develop metal oxide– and chalcogenide-based NH3 sensors with improved selectivity, humidity tolerance, and long-term stability
A fast algorithm for computing mock-Chebyshev nodes with reduced uniform grid size
No full textIn computational applications, measurements are often collected at uniformly distributed locations. In such cases, standard polynomial interpolation may exhibit divergence due to the Runge phenomenon, and the associated interpolation process is known to be severely ill-conditioned. A practical remedy is to select mock-Chebyshev points for polynomial interpolation from a dense set of equally spaced points, thereby reproducing the favorable stability properties of Chebyshev nodes. However, relatively few studies address the efficient computation of these nodes.This study proposes a new version of the fast algorithm introduced by Ibrahimoglu (A fast algorithm for computing the mock-Chebyshev nodes, Journal of Computational and Applied Mathematics, 373 (2020), 112336). The proposed approach uses the floor function to compute the ratio of distances between consecutive Chebyshev–Lobatto interpolation points. The resulting algorithm is fast and stable, consistently generating node distributions that satisfy the mock-Chebyshev requirements with linear computational complexity O(n), while reducing the size of the corresponding satisfactory uniform grid. The study also establishes a theoretical lower bound on the minimum number of equispaced nodes required to meet the mock-Chebyshev conditions. Finally, a bivariate extension to mock-Padua points on [-1,1]2 is presented and validated through numerical experiments.</p
Optimization of Bio-Based Coating Systems Containing Eucalyptus Oil, Linseed Oil, and Boric Acid for Iron Surface Protection
No full textHarnessing machine learning to mitigate water pollution in support of climate action
No full textWastewater treatment plants (WWTPs) are crucial in protecting public health and the environment by reducing pollutants before discharge into water bodies. This research presents a data-driven approach to enhance wastewater monitoring, ensuring compliance with environmental regulations by evaluating the predictive accuracy of several machine learning models in assessing effluent quality and categorizing effluent threats. In the first task, regression models such as Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM) were applied to predict Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), with Mean Absolute Error (MAE) and R-squared (R2) used as evaluation metrics. In the second task, the same models were utilized to categorize effluent threat levels, and their performance was measured through accuracy, precision, recall, and F1-score. The results demonstrate that Gradient Boosting Regressor (GBR) and AdaBoost performed well in COD prediction, achieving the lowest MAE of 6.11 and the highest R2 of 0.81. At the same time, Random Forest obtained the lowest MAE of 1.61 for BOD prediction. In the classification task, the Gradient Boosting Classifier (GBC) and AdaBoost achieved superior precision, recall, and F1 Scores, with all models attaining an overall accuracy of 97%. According to these results, machine learning methods, particularly GBC and AdaBoost, can significantly enhance prediction and classification accuracy for effluent quality, thereby improving WWTP management. This study contributes to climate resilience and sustainability by applying AI to minimize wastewater pollution, supporting SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 9 (Industry, Innovation, and Infrastructure)
Overview of hydrocolloid polymeric materials for biomedical applications: a review
No full textHydrocolloids are versatile materials widely used in many sectors such as food, wound healing systems, drug delivery systems, tissue engineering, pharmaceutical and environmental management due to their unique functional properties. Hydrocolloids are medical materials that play a vital role in the healing process and are available in various types, especially hydrocolloid wound healing systems hydrocolloid drug delivery systems and tissue engineering. These healing systems accelerate healing by providing a moist environment for wound healing, reduce pain and irritation, reduce the risk of infection and provide comfort to the patient. This article reviews the properties of hydrocolloids in the literature, their biomedical applications and the components, properties and advantages of healing systems and discusses the role of these healing systems. Hydrocolloid healing systems are made of materials such as carboxymethyl cellulose, gelatin, pectin, agar, sodium alginate, polyurethane, curdlan, Xanthan gum and QSH. This article also discusses the development of hydrocolloid healing systems, their place in modern wound care and new technological developments in this field. Proper selection and use of healing systems is critical to optimizing the healing process and this article aims to provide a comprehensive overview of this topic
Delayed detached eddy simulations of heat transfer and flow over smooth and circumferentially grooved tandem circular cylinders
No full textIn the present study, two-dimensional transient flow over circular cross-sectionalsmooth and grooved cylinders in tandem is investigated comparatively using delayed eddysimulation (DDES) based on shear stress transport (SST) k-omega turbulence model for Reynoldsnumbers 10 ≤ReD=≤ 65000 . Flow and the heat transfer characteristics are presented for variousgap ratios (1 ≤ L/D ≤ 8) between two cylinders where L and D are the distance between thecylinders and diameter, respectively. Following successful validations the study is extended to theflow past circumferentially grooved heated cylinders in tandem. It is shown that texturing thesurfaces of the both cylinders reduces the heat transfer in comparison with the smooth cylinders intandem while texturing with circumferentially grooves increases the drag coefficients of upstreamand downstream cylinders. Regardless of the Reynolds number, it is found that the critical gapratio for the grooved cylinders in tandem is L/D=4 since the Nusselt becomes minimum at this gapratio while both the drag coefficient and the Strouhal number become independent of the gap ratioafter that. </p
Tideglusib-loaded chitosan-incorporated bacterial cellulose hydrogel for enhanced palatal wound healing
No full textBacterial cellulose (BC) has been investigated for use as a wound dressing owing to its interconnected porosity structure and biocompatibility, which support tissue formation and cell interactions. Periodontal wounds left to heal secondarily after periodontal graft surgery may cause discomfort, pain, and delayed tissue repair, depending on the patient's condition. Topical application of antimicrobial treatment is recommended to avoid these problems after surgery. The purpose of this research is to investigate the influence of tideglusib (Td) when administered topically using a chitosan-incorporated BC-based hydrogel carrier on the early healing of palatal wounds in rats. The animals were randomly allocated to four groups at the commencement of the trial. Seven animals were immediately euthanized and constituted the initial group. The remaining 21 animals were split up randomly into three groups: control group, hydrogel group, and Td + Hydrogel group. The wound surface area was evaluated using photographic techniques, whereas the epithelialization rate was determined by histological investigation at seven days post-surgery. Axin2 and type I collagen expressions were assessed immunohistochemically. Statistically significant changes were identified in wound surface area and distance between epithelial borders in intergroup comparisons. Pairwise comparisons demonstrated significant differences between the baseline and Td + Hydrogel groups only. Topical use of chitosan incorporated BC-based hydrogel loaded with Td suggested a beneficial impact on the healing of palatal mucosal wounds in rats
Multi-criteria optimization of photovoltaic-based hydrogen refueling stations under behavioral and financial uncertainty: A case study
No full textThe large-scale deployment of hydrogen refueling stations remains critically constrained by renewable intermittency, temporal demand variability, and macroeconomic volatility, which is particularly acute in emerging economies such as T & uuml;rkiye. Existing studies largely rely on static or annual-average demand profiles and seldom capture the effects of high economic uncertainty, including inflation, discount rate fluctuations, and investment risk, on the techno-economic feasibility of hydrogen projects. Unlike conventional approaches, this work develops a behavior-driven, high-resolution microsimulation framework that generates realistic, temporally detailed hydrogen demand profiles synchronized with solar availability, seasonal transitions, and user refueling behavior. A five-dimensional scenario matrix, spanning electrolyzer power, hydrogen storage capacity, photovoltaic capacity and investment cost, and discount rate, enables multi-criteria optimization of the Levelized Cost of Hydrogen (LCOH), self-sufficiency, and carbon footprint under real-world economic uncertainty. Results reveal that na & iuml;ve oversizing strategies drive curtailment above 44% and LCOH beyond 11.8 /kg LCOH, 82% self-sufficiency, and up to 75% CO2 reduction. By explicitly incorporating behavioral variability, temporal demand dynamics, and macroeconomic risk, the proposed framework offers a policy-relevant, investment-oriented decision-support tool for designing hydrogen refueling stations that are cost-optimal and financially resilient, effectively bridging the gap between techno-economic modeling and real-world station deployment planning
