The Effect of the Electrode Particle Size Reduction to Nanoscale on the Low-Temperature-Operating Solid Oxide Fuel Cells
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".