Microstructure and long-term stability of Ni–YSZ anode supported fuel cells: a review
Bibliographic record
Abstract
Abstract Nickel–yttria stabilized zirconia (Ni–YSZ) cermet is the most commonly used anode in solid oxide fuel cells (SOFCs). The current article provides an insight into parameters which affect cell performance and stability by reviewing and discussing the related publications in this field. Understanding the parameters which affect the microstructure of Ni–YSZ such as grain size (Leng et al 2003 J. Power Sources 117 26–34) and ratio of Ni to YSZ, volume fraction of porosity, pore size and its distribution, tortuosity factor, characteristic pathway diameter and density of triple phase boundaries is the key to designing a fuel cell which shows high electrochemical performance. Lack of stability has been the main barrier to commercialization of SOFC technology. Parameters influencing the degradation of Ni–YSZ supported SOFCs such as Ni migration inside the anode during prolonged operation are discussed. The longest Ni-supported SOFC tests reported so far are examined and the crucial role of chromium poisoning due to interconnects, stack design and operating conditions in degradation of SOFCs is highlighted. The importance of calcination and milling of YSZ to development of porous structures suitable for Ni infiltration is explained and several methods to improve the electrochemical performance and stability of Ni–YSZ anode supported SOFCs are suggested.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".