Hydrogen production via methane partial oxidation over SBA-15-coated cordierite monolithic NiO catalysts: Synergistic effects of CeO2 and ZrO2 doping

This study investigates the effect of mesoporous silica (SBA-15) combined with nickel and metal oxides on hydrogen production. Metal, metal oxide and SBA-15 solutions were successfully coated on the surface of cordierite monolith using the wash coating method. The characterization results confirmed the structural and chemical properties of the synthesized catalysts. SEM-EDS analysis demonstrated the uniform distribution of active components within the monolithic framework, ensuring effective metal dispersion. XPS spectra identified the binding states of Ni, Ce, and Zr, elucidating the active metal species responsible for catalytic activity. BET analysis revealed that alumina-supported catalysts exhibited a pore size range of 9–13 nm along with an enhanced surface area. XRD analysis confirmed the high crystallinity of the cordierite structure and the presence of NiO, CeO2, and ZrO2 phases. TPR analysis provided insights into metal-support interactions, highlighting the reducibility of NiO species and the temperature range at which reduction occurs. The catalytic performance of the synthesized monolithic catalysts was assessed in the partial oxidation of methane at reaction temperatures of 750, 800, and 850 °C under GHSV conditions of 10,000 and 20,000 h−1. The results indicated that decreasing the GHSV to 10,000 h−1 significantly improved methane conversion due to prolonged gas-catalyst contact time, thereby enhancing reaction efficiency. Among the catalysts, SBA-15/ZrO2/Ni exhibited the highest CH4 conversion and stability, maintaining a conversion of 95.6% at 800 °C after 10 h, while demonstrating minimal coke accumulation (0.22 mg C/gsupported catalyst). Although alumina-containing catalysts initially achieved higher CH4 conversion and H2 selectivity, they exhibited greater susceptibility to coke deposition over time, which affected their long-term stability

Synthesis of Monoclinic ZrO2-Supported Cu/ZnO for Methanol Production from Carbon Dioxide

Methanol is produced via syngas, and the catalysts used are based on Cu/Zn/Al. There is no catalyst yet to produce methanol from carbon dioxide with high performance. In this study, zirconia-supported copper/zinc-based catalysts with gallium and lanthanum promoters were synthesized via incipient wetness impregnation (IWI), sol–gel (SG), and deposition–precipitation (DP) with various configurations. The samples were characterized via BET, SEM, H2-TPR, and XRD and tested for methanol production. On the BET analysis, the samples synthesized via DP method yielded the highest surface area with 51.282 m2/g. H2-TPR analysis showed that samples were reduced ideally at 250°C. The incipient wetness impregnation method was found to have several disadvantages, while sol–gel and deposition–precipitation methods yielded uniformly dispersed oxide species on the support material, and the copper and zinc species’ particle sizes strongly affected the catalytic performance of the sample as the XRD results indicated. The DP method gave the highest performance in producing methanol, and the 33Cu25Zn3Ga/MZ–DP catalyst sample acquired with the ideal amount of gallium addition as promoter was found to be giving the highest yield of methanol among all the samples (0.329 g gcatalyst−1 h−1) at 250°C, 4 MPa, and 6000 h−1

Pasta with grape leaves and hops extract: Effect on quality properties, predicted glycemic index and in vitro bio-accessibility of phenolics

Grape (Vitis vinifera L.) leaves and hops (Humulus lupulus L.) extract were used to enrich durum wheat pasta at 0.25 g and 0.5 g/100 g durum wheat semolina. Initially, the antioxidant capacities and phenolic compositions of extracts from grape leaves (GL) and hops were analyzed. Subsequently, the quality characteristics and predicted glycemic index (GIpredicted) of enriched pasta were investigated. The highest cooking loss values were observed for pasta containing grape leaves (PGL) for both addition levels and pasta with 0.5% hops (PH (0.5%)). GL incorporation caused a significant reduction in water absorption value (∼6.5%) when compared to control pasta (CP). The highest hardness value (17.14 N) was obtained for PGL (0.5%). The GIpredicted value was 45.54 ± 0.27 and 52.71 ± 0.34 for PGL (0.5%) and PH (0.5%), respectively. PGL (0.5%) and PH (0.5%) were selected for in vitro phenolic digestibility assays and sensorial evaluation due to their GIpredicted values being lower than those achieved with 0.25% extract incorporation. Protocatechuic (5500.88 μg/g), caffeic (5978.38 μg/g) and vanillic acid (2717.06 μg/g) were major phenolic acids in GL extract and they were protected during digestion. Among flavonoids, rutin was detected as 430.52 ± 1.02 μg/g for GL and 10274 ± 24.08 μg/g for hops extract while myricetin was revealed in only hops extract (367.56 ± 2.03 μg/g). No significant differences were observed between CP and PGL (0.5%) in terms of sensorial attributes except for odour based on a 9-point hedonic scale. Therefore, GL incorporation can be considered as a promising strategy to enrich pasta without affecting consumer satisfaction

An Effective Federated Learning Approach for Secure and Private Scalable Intrusion Detection on the Internet of Vehicles

The rapid proliferation of connected vehicles in the Internet of Vehicles (IoV) has introduced significant data security and privacy challenges, emphasizing the need for advanced intrusion detection systems (IDS). This article proposes a federated learning-based intrusion detection system (FL-IDS), explicitly designed to identify both external network-level threats and internal vehicular cyberattacks. Federated learning enables collaborative training across distributed vehicles without sharing raw data, significantly reducing communication overhead and preserving data privacy. To further enhance privacy, differential privacy (DP) mechanisms are applied, ensuring sensitive information remains protected even during model updates. Additionally, secure communication channels are established using Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocols, effectively safeguarding the integrity and authenticity of data exchanges between vehicles, roadside units, and cloud servers. Robust preprocessing methods, including data balancing, normalization, and feature selection, are combined with an adaptive federated learning strategy (FedXgbBagging) specifically designed to address the challenges posed by heterogeneous and non-independent and identically distributed (non-IID) data. Extensive evaluations on two real-world datasets, CSE-CIC-IDS2018 for network attacks and CICIoV2024 for in-vehicle Controller Area Network (CAN) bus attacks—show remarkable performance, achieving accuracy rates of 99.64% and 99.99%, respectively. The proposed FL-IDS significantly outperforms existing methods, demonstrating its robustness, adaptability, and scalability in securing IoV environments against diverse cyber threats

Seasonal distribution and removal efficiency of microplastics in landfill leachate treatment plants in Istanbul, Turkiye

Due to inadequate management, plastic waste accumulates in landfills and transforms into microplastics (MPs), which concentrate in leachate and pose risks to ecosystems and human health. This study examines the seasonal variation, size, type, and removability of MPs in leachate from the Kömürcüoda (LTP-1) and Odayeri (LTP-2) landfill leachate treatment plants in Istanbul. Seasonal samples from raw leachate (RL), membrane bioreactor (MBR), ultrafiltration (UF), nanofiltration (NF), and nanofiltration concentrate (NFC) units were analyzed in spring and autumn. The highest MP concentrations were found in MBR units, with 29 particles/L in autumn and 11 particles/L in spring at LTP-1. In LTP-2, MP concentrations in anoxic and aerobic units were 20 and 24 particles/L in autumn and 17 and 26 particles/L in spring. Significant MP reduction was observed in UF and NF outlets, with an MP removal efficiency of approximately 97 % between RL and NF units at both sites. The predominant MPs were blue, black, and transparent fibers, ranging from 500 to 1999 µm in autumn and 1000 to >2000 µm in spring. Polymeric analysis identified polyamide (PA) as the most prevalent material at LTP-1 (42 %), followed by polypropylene (PP) and polyisoprene (PI), while at LTP-2, PP was dominant (46 %), followed by polyethylene (PE), PI, and PA. Despite high removal efficiencies, daily MP release after treatment was estimated at 5 × 10⁵ and 13 × 10⁵ particles for LTP-1 and LTP-2, respectively, indicating continued MP discharge. The high number of MPs detected in untreated landfill leachate, along with their persistence even after treatment processes, highlights the potential accumulation and toxicity risks posed by MPs released into aquatic ecosystems. Further research should focus on understanding the long-term environmental behaviour of these pollutants in receiving environments, as well as on improving advanced treatment technologies for MP removal

Conventional and microwave drying of kefir grains: effect on drying, rehydration and fermentation kinetics and viability of kefir grains

n the present work, impacts of microwave and oven drying on the drying, rehydration, and fermentation kinetics of kefir grains as well as on the microbial viability of grains were investigated. Kefir grains were dried at various oven temperatures (30 °C, 37 °C, and 45 °C) and microwave powers (100 W, 180 W, and 300 W). Microwave drying reduced the drying time by 90 % and water absorption capacity of grains dried by microwave is higher than those dried by the oven. Moisture diffusivity was observed to increase with drying temperature (0.4&nbsp;×&nbsp;10−10–1.7&nbsp;×&nbsp;10−10 m2/s) and microwave power (3.2&nbsp;×&nbsp;10−10–17&nbsp;×&nbsp;10−10 m2/s). The results indicated that drying methods and conditions do not affect the fermentation ability and the viability of kefir grains to a detrimental extent. Compared to spray and freeze drying, the survival rate in microwave and oven drying (around 90 % for both LAB and yeast) was significantly high. As a result of the modeling studies, it was determined that the drying kinetics could be represented by the 1st order kinetic model (Lewis model) while the rehydration and fermentation kinetics could be represented by the pseudo 1st order kinetic model.Keywords:&nbsp;kefir;&nbsp;drying;&nbsp;microwave;&nbsp;rehydration;&nbsp;fermentation;&nbsp;kinetics</div

Deep Learning Approaches in the Effects of Recession and FOMC Minutes on Oil Prices

The financial industry is increasingly interested in predictive analysis and forecasting using time series data. Understanding the relationship between recessions and oil markets is crucial for developing financial forecasts and strategic decisions. This study uses advanced deep learning models to examine the interaction between recession signals and crude oil prices. Data covering recession periods include key economic indicators such as Gross Domestic Product (GDP) fluctuations, unemployment rates, consumer spending trends, business investments, and housing market dynamics. Additionally, Federal Open Market Committee (FOMC) minutes are used to capture economic assessments and monetary policy decisions by the Federal Reserve during recessions, providing insights into policymakers’ expectations and responses. Data is augmented using Time-series Generative Adversarial Networks (TimeGAN) to capture intricate patterns in oil prices. By focusing on feature selection, this study aims to identify patterns from historical data and relationships between recession signals and oil price movements. Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Transformer, and ensemble learning techniques are used to predict crude oil prices during recessions. This research provides insights into how recession signals and Federal Reserve policy decisions influence crude oil prices, offering a comprehensive view of the dynamics between economic downturns and the energy market

Synthesis of sensitizer containing triphenylamine as donor group and investigation of solar cell applicability

Developing technology and the increase in the world population increase the need for energy day by day. Renewable energy sources provide an affordable, eco-friendly way to meet this need. Solar energy, which is a renewable energy source, has sufficient capacity to obtain more affordable and clean energy compared to other sources. Researchers are interested in triphenylamine and its derivatives because of their numerous uses in solar cells, electronics, and medicine. This study, solar cell applicability was investigated by synthesizing an organic dye, in which the dye contains triphenylamine as the donor group and the hydroxyl group as the acceptor group that provides binding to TiO2. FTIR, MS, NMR, and UV-Vis spectroscopy are used to identify the compound’s (srl-2) structure. With srl-2-based DSSC and AM (amplitude modulation) irradiation (100 mW/cm2), a PCE (photoelectric conversion efficiency) value of 0.01% was attained