Understanding of Oxygen Reduction Reaction on Perovskite-Type Ba0.5Sr0.5Fe0.91Al0.09O3-δ and Ba0.5Sr0.5Fe0.8Cu0.2O3-δ Using AC Impedance Spectroscopy Genetic Programming
Bibliographic record
Abstract
Fundamental understanding of the oxygen reduction reaction (ORR) mechanism in electrochemical cells (e.g., solid oxide fuel cells (SOFCs)) is rather challenging because of several processes, which occur with similar time constants. Also, it is very difficult to elucidate ORR reaction steps using a conventional equivalent circuit modeling in ac impedance spectroscopy. There is no unique model to fully explain the ORR mechanism, especially in high temperature SOFCs. In this study, attempt has been made using impedance spectroscopy genetic programming (ISGP) technique to describe SOFC cathode ORR processes. Using ISGP, we analyzed the electrochemical performance of Ba 0.5 Sr 0.5 Fe 0.91 Al 0.09 O 3-δ (BSFAl) and Ba 0.5 Sr 0.5 Fe 0.8 Cu 0.2 O 3-δ (BSFCu) cathodes with oxide ion conducting La 0.8 Sr 0.2 Ga 0.8 Mg 0.2 O 3-δ (LSGM) and proton conducting Ba 0.5 Sr 0.5 Ce 0.6 Zr 0.2 Gd 0.1 Y 0.1 O 3-δ (BSCZGY) electrolytes. The ORR mechanism is explained by finding the distribution function of relaxation time (DFRT) with the help of ISGP. By monitoring the changes in the DFRT models at different temperatures and using both oxide ion and proton conducting electrolytes, we are able to deconvolute the ORR to its several polarization subprocesses. Using the present approach, it is possible to gain additional information, which may be convoluted and therefore undetected in conventional impedance analysis. The analysis procedure results in a direct and unambiguous DFRT model, with a distinct physical meaning. Therefore, making it especially beneficial in comparative studies, as demonstrated in this work, where it was found that BSFCu-LSGM cathode showed better charge-transfer properties than BSFAl-LSGM cathode, due to higher conductivity of BSFCu phase. In both BSCZGY-BSFCu and BSCZGY-BSFAl cathodes, the rate-limiting step was found to be the charge-transfer process, owing to low electrical (ionic) conductivity of BSCZGY.
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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".