Osmaniye Korkut Ata University

The selective oxidation of H2S under ambient conditions remains a significant challenge due to the simultaneous requirements of efficient activation and controlled sulfur formation. Herein, we report a cooperative catalytic system comprising MgO-CuO nanoparticles supported on nitrogen-doped carbon aerogels (NBCAs) derived from chitosan. The catalyst was fabricated via a freeze-drying-assisted pyrolysis process, yielding uniformly dispersed dual metal oxides within a hierarchically porous carbon framework. Combined experimental characterization and density functional theory (DFT) calculations reveal a synergistic mechanism in which CuO primarily promotes H2S dissociation and O2 activation, and MgO provides a thermodynamically favorable pathway for deep oxidation of elemental sulfur to sulfates. The optimized Mg0.6Cu0.4-NBCA-600 catalyst delivers an exceptional sulfur breakthrough capacity of 5051.4 mg g-1 at room temperature and retains over 75% of its activity after five regeneration cycles. Moreover, the catalyst exhibits stable performance under varying oxygen concentrations and humidity levels, demonstrating high durability and adaptability under realistic operating conditions. These results elucidate the cooperative roles of dual metal oxides in ambient-temperature H2S oxidation and establish a structure-activity-mechanism relationship relevant to the rational design of regenerable desulfurization catalysts with integrated sulfur recovery.

In recent years, economic policy uncertainty has initiated a new discussion in environmental economics on the main drivers of environmental degradation. The main goal is to determine whether economic policy uncertainty leads to environmental damage or contributes to environmental quality. For this purpose, researchers commonly employ panel data analyses based on group estimation and use carbon emissions as a proxy for environmental indicators. However, by doing so they overlook country-specific estimations as well as underrepresent the ecological balance. To overcome these shortcomings, in this paper we employ two new approaches. First, we apply novel Fourier bootstrap autoregressive distributed lag estimation, which is the stronger estimation procedure in time-series analysis, to detect individual outcomes. Second, we use the ecological footprint as a proxy for environmental degradation, which reflects the natural balance more holistically and comprehensively than pollution indicators. In this context, our paper examines the impact of economic policy uncertainty on ecological footprint by using some control variables, such as economic growth and energy consumption. Our sample consists of seven emerging countries from 1965 to 2022. Fourier ARDL test results reveal a strong long-run relationship between ecological footprint and economic policy uncertainty, economic growth, and energy consumption for four emerging countries: India, Indonesia, Russia, and Türkiye. The estimations reveal that economic policy uncertainty in these countries contributes to environmental quality in the long run. In this context, it is important for policymakers to implement environmentally friendly growth strategies far from any uncertainty for the sake of sustainable economic development.

In this study, social media addiction, which attracts the attention of researchers today and is a risk factor for adolescents, was examined as a predictor of subjective well-being, which is accepted as one of the dimensions of functionality for mental health, especially within the framework of a positive approach. While explaining this relationship, the variables of social self-efficacy and school engagement, which again contribute to the psycho-social development area of ​​adolescence as a supporter, were also included in the process as mediator variables. The sample of the study consists of 821 students studying in high schools. SPSS 27.0 program was used for the correlation analyses of the estimated model of the study, and PROCESS Macro (Model-6) was used for mediator analyses. As a result of the analyses, it was seen that level of social media addiction was negatively and slightly significantly related to subjective well-being and social self-efficacy and school engagement, which were included as mediator variables. Correlation findings showed that subjective well-being was also positively and moderately significantly related to social self-efficacy and school engagement. It was also seen that there was a positive and moderately significant relationship between the mediator variables of the study (social self-efficacy and school engagement). Finally, in the analysis of the study, the indirect effect of social media addiction’s level on subjective well-being was evaluated through both social self-efficacy and school engagement, and the relationship was found to be significant.

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in & Scedil;anl & imath;urfa Province, T & uuml;rkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters-Temperature, chlorine (Cl-), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO2-), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), Free Chlorine (Cl2), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), fluoride (F-), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform-were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R2 value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions.

Cyanide is a highly toxic anion. Humans are commonly exposed to cyanide through the consumption of drinking water or by eating plants that contain cyanogenic glucosides, such as cassava, a key carbohydrate source in many diets. While numerous methods exist for cyanide detection, few are both cost-effective and simple enough for use by individuals without specialized training. In this work, we designed and synthesized a hemicyanine-based fluorescent sensor ZM-FES for the detection of cyanide (CN−). Among the anions tested, the sensor showed high selectivity towards CN−. The addition of CN− to ZM-FES in aqueous solution (ACN/H2O (1:1, v/v)) caused a dramatic decrease in the absorbance and fluorescence intensities. The detection mechanism was based on nucleophilic addition between CN− and the indolium group, which was confirmed by 1H NMR and mass spectral analysis. The fluorescence intensity plot as a function of CN− concentration showed a good linear relationship in the range 0–10 µM, and the detection limit was calculated as 0.195 µM. Furthermore, the proposed detection approach can operate over a wide pH range from 2.1 to 9.3. Finally, ZM-FES was successfully utilized to detect CN− in food samples and satisfactory results were obtained.