Synthesis and Characterization of NiO–YSZ Anode Materials: Precipitation, Calcination, and the Effects on Sintering
Bibliographic record
Abstract
Nickel oxide yttria‐stabilized zirconia (NiO–YSZ) anode materials were synthesized via hydrolysis of the corresponding chloride solutions with NH 3 , NH 3 +NaOH, and NaOH as precipitation agents. Powder properties such as crystallite size, morphology, and sintering behavior of the final NiO–YSZ materials were also studied. The mechanism of the formation of NiO–YSZ was established for the different co‐precipitation techniques by the direct observation of Ni(NH 3 ) n +2 complexes, Ni(OH) 2 and NiO at different stages of the synthesis process. A direct relationship between the precipitation agent, the order of calcination from dry sample to final product, the final composition, the crystallite sizes and particle sizes of NiO, and the sinterability of the final products was established. A comparison of the powder and individual component properties indicate that the choice of precipitation agent greatly influences the final characteristics. Ni/YSZ materials prepared by NH 3 +NaOH precipitation offer higher Ni dispersion and nanocrystallinity of both the Ni and YSZ phases. The conductivity of both prepared materials compares well with mixed‐oxide materials of higher Ni content.
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 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) | 0.001 | 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".