Oxygen Reduction Reaction Properties of Cobalt-Free Perovskites for SOFCs
Bibliographic record
Abstract
Doping is one of the pathways through which electronic and ionic conductivity can be tuned in metal oxides. In this study, the oxygen reduction reaction (ORR) properties of Ba0.5Sr0.5Fe0.91Al0.09O3-δ (BSFAl), Ba0.5Sr0.5Fe0.8Cu0.2O3-δ (BSFCu) and Ba0.5Sr0.5Fe0.8Nb0.2O3–δ (BSFNb) are investigated using proton conducting (Ba0.5Sr0.5Ce0.6Zr0.2Gd0.1Y0.1O3-δ, BSCZGY) and oxide ion conducting (La0.8Sr0.2Ga0.8Mg0.2O3-δ, LSGM) electrolytes. BSFAl and BSFCu were synthesised through combustion method, whereas BSFNb was synthesised through solid-state method. BSFCu-LSGM/LSGM/BSFCu-LSGM symmetrical cell showed lower area specific resistance (ASR) of 0.077 Ω cm2 in air at 850 °C than BSFAl-LSGM/LSGM/BSFAl-LSGM cell. In air medium, symmetrical cells with LSGM exhibited lower ASR values than BSCZGY symmetrical cells owing to its higher ionic conductivity. In order to understand more about the oxygen consumption and release properties in these oxides, thermogravimetric analysis in varying pO2 were performed. In all pO2, BSFAl showed the highest weight loss and gain during heating and cooling cycles, while BSFNb showed the lowest weight gain and loss. Detailed discussion on oxygen nonstoichiomtery and ORR of Co-free perovskite-type oxides will be presented in this talk.
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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".