Effects of pH and calcination temperature on gel-combustion synthesizability of BaZr0.8Y0.2O3 perovskite
Bibliographic record
Abstract
Solid oxide fuel cells with their advantages such as high efficiency are now considered as efficient power generation equipment. Because of its proton conductivity, perovskite is used in ceramic fuel cell electrolyte, and the addition of dopant can improve its proton conductivity. In this research, BaZr0.8-xSrxY0.2O3 (x=0, 0.05, 0.1, and 0.15) perovskites were synthesized by gel-combustion method. Barium nitrate, zirconium nitrate, yttrium nitrate and strontium nitrate were used as raw materials. Based on DTA and TGA analyses, the required temperature for calcination was determined to be around 1000 °C. XRD and FTIR analyses were used to identify the phases. The synthesis was carried out under different conditions and the effects of pH and dopant percentage on the morphology and size of the particles were investigated by FESEM. The sintering process was completed at different temperatures and a relative density of 94% was obtained at 1470 °C.
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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 | 0.001 |
| 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".