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Creators/Authors contains: "Lee, Taehee"

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  1. ; ; ; ; ; ; ; ; (, Journal of Materials Chemistry A)
    A redox-reversible BLFMN material demonstrates good performance and coking resistance as the fuel electrode in SOFCs. 
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  2. ; ; ; ; ; ; ; ; (, ACS Applied Materials & Interfaces)
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  3. ; ; ; ; ; ; ; (, Clean Energy)
    Abstract Solid-oxide fuel cells (SOFCs) offer great promise for producing electricity using a wide variety of fuels such as natural gas, coal gas and gasified carbonaceous solids; however, conventional nickel-based anodes face great challenges due to contaminants in readily available fuels, especially sulphur-containing compounds. Thus, the development of new anode materials that can suppress sulphur poisoning is crucial to the realization of fuel-flexible and cost-effective SOFCs. In this work, La0.1Sr1.9Fe1.4Ni0.1Mo0.5O6–δ (LSFNM) and Pr0.1Sr1.9Fe1.4Ni0.1Mo0.5O6–δ (PSFNM) materials have been synthesized using a sol-gel method in air and investigated as anode materials for SOFCs. Metallic nanoparticle-decorated ceramic anodes were obtained by the reduction of LSFNM and PSFNM in H2 at 850°C, forming a Ruddlesden–Popper oxide with exsolved FeNi3 bimetallic nanoparticles. The electrochemical performance of the Sr2Fe1.4Ni0.1Mo0.5O6–δ ceramic anode was greatly enhanced by La doping of A-sites, resulting in a 44% decrease in the polarization resistance in reducing atmosphere. The maximum power densities of Sr- and Mg-doped LaGaO3 (LSGM) (300 μm) electrolyte-supported single cells with LSFNM as the anode reached 1.371 W cm −2 in H2 and 1.306 W cm–2 in 50 ppm H2S–H2 at 850°C. Meanwhile, PSFNM showed improved sulphur tolerance, which could be fully recovered after six cycles from H2 to 50 ppm H2S–H2 operation. This study indicates that LSFNM and PSFNM are promising high-performance anodes for SOFCs. 
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  4. ; (, Proceedings of the 9th International Workshop on Climate Informatics)
    To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone (UK'37) based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric approach not only deals with the variable pattern of noises over SSTs but also contains a Bayesian method of classifying potential outliers. 
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