Electrochemical Pressure Impedance Spectroscopy As a Diagnostic Method for Hydrogen-Air Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
This work introduces a novel in-situ diagnostic method for polymer electrolyte fuel cells (PEFCs) referred to as electrochemical pressure impedance spectroscopy (EPIS). Inspired by electrochemical impedance spectroscopy (EIS), EPIS is a spectroscopic technique that analyses correlations between frequencies in an applied pressure signal and the corresponding voltage response signal in the frequency domain. In EPIS, cathode cell pressure is modulated as a sinusoidal wave by the back-pressure controller (BPC), and the cell voltage is monitored as the response signal. Cell voltage response originates from the membrane electrode assembly (MEA) and, therefore, EPIS can be used to probe cathode transport properties and is sought to develop tools that can be used to study water transport as a function of changes in structure and operating conditions. The cathode reactant gas flows through channels with a pressure drop and, thus, the pressure signal is not identical in different sections of the flow channel. Before studying the relationships between pressure and voltage, it is important to consider the impact of the flow channel on oscillations in the pressure response. The response of the system as a function of correlations between the cathode inlet pressure and the cathode outlet pressure is examined in this work to probe the contributions of the flow channel. Due to instrumental limitations, the BPC can only perform tests at a frequency of 0.1 Hz. Experimental limitations like cathode stoichiometry ratio, cathode flow rate, oxygen partial pressure at cathode, and pressure oscillation amplitude are intensively studied, and therefore, EPIS experimental formalism is defined in this work. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".