Evolutionary Programming Based Approach for SOFC Cathode Characterization: A Case Study on Co-Free Mixed Conducting Perovskites
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a powerful technique to characterize the performance of solid oxide fuel cells (SOFCs). Given a proper analysis technique, EIS data can shed light on the different contributions to the total impedance. The most common analysis technique is equivalent circuit modelling (ECM), where a combination of resistors, capacitors, constant phase elements, etc. represents the physical response of the sample. ECM has its known drawbacks, e.g. non-uniqueness and obscure physical meaning of the chosen model. Using impedance spectroscopy genetic programming (ISGP), based on evolutionary programming, those challenges are better tackled. Our analysis focuses on finding the distribution function of relaxation times (DFRT). The analysis procedure using ISGP yields a DFRT model comprised of known mathematical peaks. Each peak in the model is characterized by its height, width and relaxation time. It is possible to assign each peak to one or more process, and to calculate its area. Thus, we are able to determine the contribution of each process to the total impedance. By plotting the DFRT as a function of frequency it is possible to identify the different polarization processes and gain additional information, which may be convoluted and therefore undetected in other analysis techniques. Here, we report the electrochemical performance of perovskite-type Co-free cathodes such as Ba0.5Sr0.5Fe0.91Al0.09O3-δ, Ba0.5Sr0.5Fe0.8Cu0.2O3-δ, and Ba0.5Sr0.5Fe0.8Nb0.2O3-δ with oxide ion conducting La0.8Sr0.2Ga0.8Mg0.2O3-δ (LSGM) and proton conducting Ba0.5Sr0.5Ce0.6Zr0.2Gd0.1Y0.1O3-δ electrolytes have been investigated by ISGP. Composites of cathode with electrolytes were screen printed on both sides of electrolyte in order to get symmetrical cells for evaluating cathode performances. EIS of symmetrical cells at different experimental conditions were analyzed by ISGP. Thus, by changing parameters and monitoring corresponding changes in the peaks’ relaxation times and areas, in-depth understanding of the operation of various components of the symmetrical cells such as the oxygen reduction reactions, mass transfer limitations and the electrolyte ionic conductivity can be achieved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".