Electrochemical Pressure Impedance Spectroscopy As a Diagnostic Method for Hydrogen-Air Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Water transport in an operating hydrogen-air fuel cells has gathered the interests of both researchers and developers working on fuel cells. Better water removal can extend the operation regime of fuel cells and, therefore, increase its maximum cell power and overall energy efficiency. However, state-of-art in situ fuel cell diagnostics cannot provide an insight into the water transport phenomenon. Recent research shows that pressure controlling techniques have the potential to address issues with water transport. The purpose of this work is to develop an in-situ diagnostic tool from cathode pressure oscillations for hydrogen-air polymer electrolyte fuel cells named as electrochemical pressure impedance spectroscopy (EPIS). The response between the cell voltage and the pressure oscillations is analogous to the electrochemical impedance spectroscopy (EIS)(See image). The relation between EPIS response and water transport phenomenon is intensively studied in this work. With the help from our industrial partner, Greenlight Innovation Corp., we are able to integrate this diagnostic method into a fuel cell test station. Experimental data are fitted by the mathematical derivation of the response from theoretical cell components from which are extracted crucial parameters and diagnostic information for water transport. Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".