分类： 化学 >> 物理化学 提交时间： 2017-11-05 合作期刊: 《结构化学》
摘要： Many researchers have studied on perovskite oxide for its unique structure. Perovskite oxides, ABO3-δ, with different A and B metals have shown wide applications in many fields, in particular solid oxide electrolysers. SrFeO3-δ, typical perovskite oxides, in which iron is the mixed-valence cation with the capacity to change the chemical valence, have a wide range of oxygen nonstoichiometry. In this study, Sr0.9Fe0.9Zr0.1O3-δ (SFZO) is synthesized and then treated in 5%H2/Ar and air at high temperature, exhibiting excellent redox stability. Redox-stable structure, oxygen vacancy and electrical properties of SFZO are investigated. Steam electrolysis is then performed with SFZO cathode under 5%H2O/5%H2/Ar and 5%H2O/Ar atmospheres, respectively. The present results indicate that the SFZO is a novel promising cathode material for solid oxide steam electrolyser.
分类： 核科学技术 >> 粒子加速器 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： In energy dispersive X-ray fiuorescence (EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm (GA) and back propagation (BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.
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