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Record W2466895675 · doi:10.1149/ma2016-02/39/2897

The Effect of the Electrode Particle Size Reduction to Nanoscale on the Low-Temperature-Operating Solid Oxide Fuel Cells

2016· article· en· W2466895675 on OpenAlexaff
Jung Hoon Park, Seung Min Han, Jongsup Hong, Byung-Kook Kim, Hae-Weon Lee, Hyoungchul Kim, Kyung Joong Yoon, Jong‐Ho Lee, Ji‐Won Son

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceAnodeNanostructureElectrodeElectrolyteOxideNanotechnologyOperating temperatureSinteringSolid oxide fuel cellThin filmNanoparticleOptoelectronicsChemical engineeringMetallurgyElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

There have been intensive research activities to lower the operating temperature of solid oxide fuel cells (SOFC) to avoid problems related to the high-temperature (≥ 800 oC) operation, such as reliability and cost issues, and to expand the application fields toward portable and mobile power sources. In this regard, thin-film electrolytes and nanostructure electrodes have been at the center of interests to reduce the ohmic and polarization losses while decreasing the operating temperatures. The effect of thinning down the electrolyte is straightforward, however, that of particle size reduction at the electrode is rather complicated. One of the main reasons is the difficulty of elucidating various electrode reaction mechanisms. Another reason can be the difficulty of fabricating the nanostructure electrodes. If conventional powder processing is used, it is challenging to reduce the electrode particle size due to the original particle size of the starting powder and high-temperature sintering. On the other hand, in common thin-film-based SOFCs, nanostructure noble metal electrodes, such as Pt, are employed, thus it is difficult to interrogate the effect of the particle size reduction of the widely used SOFC electrodes. Owing to the research efforts during the last decade, we have been able to obtain high-performance low-temperature-operating SOFCs (LT-SOFCs) based on the anode-supported platform by implementing thin-film electrolytes and nanostructure electrodes, while using the common SOFC materials. A peak power density as high as 600 mW cm-2 at 500 oC was achieved based on the nanostructure Ni-YSZ anode, ~1 micron-thick YSZ-GDC bilayer electrolyte, and nanostructure LSC or LSC-GDC composite cathode. The active cell components are fabricated by using pulsed laser deposition (PLD) and the particle size of the anode is 100-200 nm, that of the cathode is around 10-several 10s nm. Since the most interested topic regarding the LT-SOFCs has been the cell power output, the study on the effect of nanostructure electrodes at LT is rather not intensively performed. Therefore, in the current presentation, we will exhaustively review and discuss the impact and influence of the particle size reduction to nanoscale at both the cathode and the anode on LT-SOFCs. The distinctive characteristics of the nanoscale electrodes will be presented based on the half-cell and full-cell tests, and the direct comparisons between the cells in which the only difference is the electrode particle size. Acknowledgement The authors are grateful to the Global Frontier R&D Program on Center for Multiscale Energy Systems (Grant No. NRF-2015M3A6A7065442) of the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT & Future Planning (MSIP), and to the Institutional Program (2E26081) of Korea Institute of Science and Technology (KIST) for financial support. References J.-H. Park, W.-S. Hong, G. C. Kim, H. J. Chang, J.-H. Lee, K. J. Yoon, and J.-W. Son, J. Electrochem. Soc, 160, F1027 (2013) H.-S. Noh, K. J. Yoon, B.-K. Kim, H.-J. Je, H.-W. Lee, J.-H. Lee, and J.-W. Son, J. Power Sources, 247, 105 (2014) J.-H. Park, W.-S. Hong, K. J. Yoon, J.-H. Lee, H.-W. Lee, and J.-W. Son, J. Electrochem. Soc., 161, F16 (2014) J. H. Park, S. M. Han, K. J. Yoon, H. Kim, J. Hong, B.-K. Kim, J.-H. Lee, and J.-W. Son, J. Power Sources, 315, 324 (2016)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.243
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
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