Optimization of Infiltration Techniques Used to Construct Ni/YSZ Anodes
Bibliographic record
Abstract
A range of Ni-containing solutions have been infiltrated into a symmetrical tubular half-cell composed of a slip-casted, porous yttria-stabilized zirconia (YSZ) anode support, Ni-YSZ functional layers, a YSZ electrolyte, and a second, outer porous YSZ anode support layer, aiming at the development of high performance anodes that are tolerant to redox-cycling. A combination of surface wettability experiments and optical and electron microscopy imaging has been used to determine how well these solutions penetrate the porous YSZ matrix, then correlating these results with the electrochemical performance in humidified H2 environments at 800 ⁰C. It is shown that the addition of the Triton-X-100 surfactant to the infiltration solution results in excellent penetration of the YSZ matrix, while the use of urea as a Ni complexing agent does not give good wettability, thus leaving a Ni-rich layer on the outer anode surface. Overall, the use of a two-step process, involving several infiltrations with Ni nitrate solutions containing Triton-X-100, followed by several infiltrations with urea-containing solutions, leads to the best cell performance as well as the best Ni distribution inside the anode layers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".