Developing excellent-performance cathode is an inevitable and challenging obstacle for developing practical solid oxide fuel cells, especially in low temperature range. In this paper, we introduce barium into the premium SrCoO3-based cathodes and research its consequent effect in the oxygen reduction reaction process. It is observed that the barium dopant could expand the lattice, produce more oxygen vacancies and reduce the thermal expansion coefficient of parent perovskite, which results from the expanded free space for oxygen migration and weakened Co-O bond. The polarization resistances of single cells with Sr1-xBaxCo0.8Sc0.1Nb0.05Ti0.05O3-δ cathodes (x=0, 0.2 and 0.5, as SCSNTi, SB0.2CSNTi and SB0.5CSNTi, correspondingly) are 0.038, 0.025 and 0.016 Ω cm2 at 600 °C, respectively, and corresponding peak power density of 0.852, 1.054 and 1.217 W cm-2. The SB0.5CSNTi cathode can stably operate for 200 h at 550 °C. And it also exhibits excellent performance using CH4 as fuel and good stability for 100 h at 600 °C. First principle calculations prove that the Ba doping effectively reduces the oxygen vacancy formation energy and oxygen migration barrier. This work proves that the lattice expansion effect is an effective strategy for accelerating the oxygen exchange rate and designing high-performance cathode materials.
Nowadays, the temperature gradient is considered as one of the most important parameters which impact the performance of the solid oxide fuel cell (SOFC). In this paper, a control strategy based on an input-output feedback linearization technology is derived for controlling the maximum temperature gradient within the anode fuel flow channel at the desired value. For the controller design, the temperature dynamic model is proposed and simplified to a control-oriented multi-input and multioutput nonlinear dynamic model. Then, this paper presents an input-output feedback linearization controller to realize the control objective by adjusting the cathode input air flow. Finally, the simulation results are given to demonstrate the accuracy of the proposed model in reflecting the temperature dynamic characteristics. Moreover, the compound controller is added for simulation as a comparison, which shows that the proposed controller is equipped with better effectiveness and efficiency in the presence of external disturbances.
Efficient nitrogen reduction reaction (NRR) catalysis relies on the active sites of the electrocatalyst being capable of adsorption of hydrogen ions and nitrogen molecules.
Sintering of Ni particles in the Ni/Y-doped ZrO 2 anode is a major obstacle to the widespread use of solid oxide fuel cell. In this study, we investigated the dopant effect on the diffusion of a Ni atom on the ZrO 2 surface with dopants (Y and Al) by density functional theory calculations in order to inhibit the sintering. The most stable adsorption sites of the Ni atom on the Al-doped and Y-doped ZrO 2 surfaces are the vicinity of the twofold-coordination oxygen atom and the vicinity of an oxygen vacancy, respectively. It is found that the most stable adsorption energy on the Al-doped ZrO 2 surface is larger than that on the Y-doped ZrO 2 surface. The analysis of diffusion path based on the potential energy surfaces of the Ni atom on the two surfaces shows that the energy barrier for the diffusion of the Ni atom on the Al-doped ZrO 2 surface is larger than that on the Y-doped ZrO 2 surface. The diffusion of the Ni atom on the Al-doped ZrO 2 surface is more difficult than that on the Y-doped ZrO 2 surface. This is because the Ni atom strongly bound to the twofold-coordination oxygen atom and the Ni atom is constrained in the Al-doped ZrO 2 surface. Thus, the Ni sintering on the Al-doped ZrO 2 surface is inhibited compared to that on the Y-doped ZrO 2 surface.
Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.
We have developed a molecular dynamics (MD) simulation method to investigate the sintering of nickel nanoparticles in the nickel and yttria-stabilized zirconia (Ni/YSZ) anode of a solid oxide fuel cell (SOFC). The conventional sintering model consists of only two or three nickel nanoparticles. Therefore, it does not reflect the properties of the porous structure of the Ni/YSZ anode or reproduce realistic sintering. Our Ni/YSZ multi-nanoparticle MD simulation method uses a multi-nanoparticle model based on the porosity and Ni/YSZ nanoparticle ratio of a realistic anode. The Ni and YSZ nanoparticles are packed randomly in the simulation cell, and compressed to achieve the correct porosity. Furthermore, because the reliable potential parameters for MD simulation between nickel and YSZ have not been reported, we determine reliable interatomic potential parameters between nickel and YSZ by using the nonlinear least-squares method to fit the Morse potential function to interaction energies obtained by density functional theory. The sintering simulation using our Ni/YSZ multi-nanoparticle model and our potential parameters reveal that the YSZ nanoparticle framework suppresses the sintering of nickel nanoparticles by disrupting the growth of the neck between two nickel nanoparticles. The previously reported model of two nickel nanoparticles did not produce these results. Our multi-nanoparticle MD simulation method is effective for investigating the realistic sintering process in the porous structure of the Ni/YSZ anode and for designing durable anode structures for SOFCs.
Abstract Wear of contact materials results in energy loss and device failure. Conventionally, wear is described by empirical laws such as the Archard's law; however, the fundamental physical and chemical origins of the empirical law have long been elusive, and moreover empirical wear laws do not always hold for nanoscale contact, collaboratively hindering the development of high‐durable tribosystems. Here, a non‐empirical and robustly applicable wear law for nanoscale contact situations is proposed. The proposed wear law successfully unveils why the nanoscale wear behaviors do not obey the description by Archard's law in all cases although still obey it in certain experiments. The robustness and applicability of the proposed wear law is validated by atomistic simulations. This work affords a way to calculate wear at nanoscale contact robustly and theoretically, and will contribute to developing design principles for wear reduction.
Background Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.
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