Oxides are widely used for energy applications, as solid electrolytes in various solid oxide fuel cell devices or as catalysts (often associated with noble metal particles) for numerous reactions involving oxidation or reduction. Defects are the major factors governing the efficiency of a given oxide for the above applications. In this paper, the common defects in oxide systems and external factors influencing the defect concentration and distribution are presented, with special emphasis on ceria (CeO ) based materials. It is shown that the behavior of a variety of oxide systems with respect to properties relevant for energy applications (conductivity and catalytic activity) can be rationalized by general considerations about the type and concentration of defects in the specific system. A new method based on transmission electron microscopy (TEM), recently reported by the authors for mapping space charge defects and measuring space charge potentials, is shown to be of potential importance for understanding conductivity mechanisms in oxides. The influence of defects on gas-surface reactions is exemplified on the interaction of CO and H O with ceria, by correlating between the defect distribution in the material and its adsorption capacity or splitting efficiency.
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http://dx.doi.org/10.1002/adma.201706300 | DOI Listing |
Sci Rep
December 2024
Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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December 2024
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications.
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December 2024
Department of Organic Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, 47416-95447, Iran.
The oxidation of 5-HMF to HMFCA is an important yet complex process, as it generates high-value chemical intermediates. Achieving this transformation efficiently requires the development of non-precious, highly active catalysts derived from renewable biomass sources. In this work, we introduce UoM-1 (UoM, University of Mazandaran), a novel cobalt-based metal-organic framework (Co-MOF) synthesized using a simple one-step ultrasonic irradiation method.
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December 2024
School of Materials Science and Engineering, Liaocheng University, Liaocheng, 252059, Shandong, China.
The welding of titanium alloys is an important topic in today's industrial field, and the interaction between the solder and the base material is crucial for the quality of the welded parts. The structural, elastic, electronic, and thermal properties of Ti-Al-Me (Me = Cu, Fe and Ni) alloys (TAMs) with the face-centered cubic structures were investigated using plane-wave pseudo potential method in the framework of density functional theory. Based on the calculated elastic constants combined with empirical and semi-empirical formulas, physical properties including ductility/brittleness, hardness and anisotropy were calculated.
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December 2024
Department of Physics, Shahid Beheshti University, Tehran, 1983969411, Iran.
Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities.
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