Bibliographic record
Abstract
A number of perovskite oxides, typically, heavily doped SrTiO₃ samples, were synthesized and characterized with a view to establishing their potential as anode materials for solid oxide fuel cells (SOFCs). The structure, microstructure, electrical conductivity, reduction-oxidation behavior, phase stability, compatibility with electrolytes, and performance in SOFC operation were assessed. Ceramic samples were prepared with the formula (Sr₁ᵪRᵪ)(Ti₁_yTy)O₃ (R = rare earth elements, T = transition metals) and with charge balance achieved by A-site deficiency. Electrical conductivities were examined by the do four-probe method and impedance spectroscopy. It was found that yttrium is soluble in SrTiO₃ (SYT) up to 8 mol% and has marked effects on conductivity. Electrical conductivities were observed to increase with increasing donor-doping level, on reduction in low oxygen partial pressures. Electrical conductivity with values as high as 82 S/cm was achieved at 800°C and P(O₂) = 10ˉ¹⁹ atm. Electrical conductivities were reversible upon reduction and oxidation. The thermal expansion coefficient is compatible with electrolyte materials such as yttria-stabilized ZrO₂ and doped LaGaO₃. Cobalt-doped SYT, which showed a relatively high resistance to oxidation, was tested as the anode material in a fuel cell. Yttrium-doped SrTiO₃ meets the requirements for the anode in SOFCs to a substantial degree, and is a promising alternative anode material.
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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) | 0.000 | 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".