Preparation and Characterization of Component Materials for Intermediate Temperature Solid Oxide Fuel Cell by Glycine-Nitrate Process

La1-xSrxGa1-y MgyO3-δ(LSGM) electrolyte, La1-xSrxCr1-y MnyO3-δ( LSCM ) anode and La1-xSrxFe1-y MnyO3-aaaaaaa(LSFM) cathode materials were all synthesized by glycine-nitrate process (GNP). The microstructure and characteristics of LSGM, LSCM and LSFM were tested via X-ray diffraction(XRD), scanning electron microcopy (SEM), A C impedance and four-probe direct current techniques. XRD shows that pure perovskite phase LSGM electrolyte and electrode (LSCM anode and LSFM cathode) materials were prepared after being sintered at 1400℃for 20 h and at 1000℃for 5 h, respectively. The max conductivities of LSGM (ionic conductivity), LSCM (total conductivity) and LSFM (total conductivity) materials are 0.02, 10, 16 S·cm-1 in the air below 850℃, respectively. The conductivity of LSCM becomes smaller when the atmosphere changes from air to pure hydrogen at the same temperature and it decreases with the temperature like metal. The porous and LSGM-based LSCM anode and LSFM cathode films were prepared by screen printing method, and the sintering temperatures for them were 1300 and 1250℃, respectively. LSGM and electrode (LSCM and LSFM) materials have good thermal and chemical compatibility.

ATOMIC AND NUCLEAR PROPERTIES OF MATERIALS

Table 6.1 Abridged from pdg. ibl.gov/AtomicNuclearProperties by D. E. Groom （2007）. See web pages for more detail about entries in this table including chemical formulae, and for several hundred other entries. Quantities in parentheses are for gases at 20℃ and 1 atm, and square brackets indicate evaluation at 0℃ and 1 atm. Boiling points are at 1 atm.

Machine learning for advanced energy materials

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.