The effect of copper oxide on the CuO–NiO/CeO2 structure and its influence on the CO-PROX reaction

The effect of copper oxide species on the CuO–NiO/CeO₂ structure and the influence on the preferential CO oxidation in H₂ excess (CO-PROX) reaction at low and high temperatures were investigated. Temperature-programmed surface reaction (TPSR), In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and X-ray photoelectron spectroscopy (XPS) results allowed determining the surface species. The maximum temperature of CO₂ formation or selectivity decreased about 40 °C for the CuO–NiO/CeO₂ catalyst compared to the NiO/CeO₂, which suggests that the addition of Cu⁺ species increases the active sites due to interaction with the Ni–Ce structure. Therefore, the activity of the catalyst was closely related to the oxygen in vacancies and the formation of Cu⁺-carbonyl species of the redox mechanism. Besides, the superior selectivity towards CO₂ below 150 °C depends on the carbonyl stabilization at the surface, inhibiting the adsorption and subsequent oxidation of H₂. Using TPSR and spectroscopic analyzes by DRIFTS and XPS allowed us to propose the reaction mechanisms for low and high temperatures.     

Review: the latest advances in biomedical applications of chitosan hydrogel as a powerful natural structure with eye-catching biological properties

Journal of Materials Science - Chitosan is one of the natural cationic polymers with unique properties such as non-toxicity, biodegradability, biocompatibility, environmentally friendly that has...     

GOP-SDN: an enhanced load balancing method based on genetic and optimized particle swarm optimization algorithm in distributed SDNs

Wireless Networks - One of the biggest challenges of distributed software defined networks (SDNs) is to create load balancing on controllers to reduce response time. Although recent studies have...     

Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles

Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary to effectively manage groundwater resources. We develope an unsupervised modeling approach to characterize and cluster hydrographs on regional scale according to their dynamics. We apply feature-based clustering to improve the exploitation of heterogeneous datasets, explore the usefulness of existing features and propose new features specifically useful to describe groundwater hydrographs. The clustering itself is based on a powerful combination of Self-Organizing Maps with a modified DS2L-Algorithm, which automatically derives the cluster number but also allows to influence the level of detail of the clustering. We further develop a framework that combines these methods with ensemble modeling, internal cluster validation indices, resampling and consensus voting to finally obtain a robust clustering result and remove arbitrariness from the feature selection process. Further we propose a measure to sort hydrographs within clusters, useful for both interpretability and visualization. We test the framework with weekly data from the Upper Rhine Graben System, using more than 1800 hydrographs from a period of 30 years (1986-2016). The results show that our approach is adaptively capable of identifying homogeneous groups of hydrograph dynamics. The resulting clusters show both spatially known and unknown patterns, some of which correspond clearly to external controlling factors, such as intensive groundwater management in the northern part of the test area. This framework is easily transferable to other regions and, by adapting the describing features, also to other time series-clustering applications.