Development of Monolithic YSZ Porous and Dense Layers through Multiple Slip Casting for Ceramic Fuel Cell Applications
Bibliographic record
Abstract
In this research YSZ porous support and thin dense functional electrolyte layers are fabricated via a slip‐casting method for fuel cell applications. The results show that calcination of YSZ starting powder is a crucial step in developing an effective porous structure having an interconnected void network for fuel cell applications. It is found that due to high surface area and high sinterability, as‐received Tosoh YSZ powder is not a suitable candidate for production of interconnected porous structures even with the addition of pore former. Calcination at 1300–1500°C coarsens the YSZ powder and leads to the growth of particles to 15–100 μm. However, subsequent ball milling of the calcined powder for 72 h reduces the particle size to 400–800 nm (∼240 nm for uncalcined Tosoh YSZ) and increases the subsequent surface area, correlating with the temperature of calcination. With high‐temperature calcination, it is possible to generate interconnected porous structures after sintering at temperatures lower than the calcination temperature. The bodies made of powder calcined at 1300°C become dense following sintering at 1350°C, which makes this material suitable as a dense fuel cell electrolyte. Using calcined powders, a multiple slip‐casting procedure enables a dense electrolyte to be coated directly on a highly porous support. With this simple and inexpensive technique, high‐quality strongly adhered YSZ layers can be engineered.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".