Effects of Fuel Cell Operating Conditions on Electrochemical Pressure Impedance Spectroscopy Diagnostics
Bibliographic record
Abstract
Electrochemical pressure impedance spectroscopy (EPIS) is a fuel cell diagnostic technique based on pressure alternating frequency response analysis. This technique has similarities to electrochemical impedance spectroscopy except it implements mechanical perturbations via pressure oscillations rather than voltage or current oscillations to achieve an electrochemical impedance response. This study examines pressure-induced electrochemical resistances while independently varying fuel cell inlet operating conditions, such as cathode relative humidity, gas stoichiometry at the cathode, and fuel cell temperature. Results indicated that EPIS resistances increased with decreasing cathode relative humidity, decreasing cathode stoichiometry, and increasing cell temperature. Variation in transport resistance with cathode stoichiometry was attributed to decreased oxygen partial pressures measured at the cathode outlet. Alternatively, variations in resistance with relative humidities were correlated to changes in humidity ratios throughout the cathode. Resistance changes with cell temperature were a function of both phenomena. Mechanical responses were analyzed by measuring inlet pressure response relative to outlet pressure perturbations. Mechanically based variations observed during cathode stoichiometry tests indicated additional down-channel variations from changes in gas flow. In comparison, insignificant variations in mechanical response were observed during relative humidity and temperature tests, suggesting purely through-plane responses attributed to electrochemical resistances within the membrane electrode assembly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".