The Use of the Anode Water Removal Method to Understand Cathode Gas Diffusion Layer Flooding
Bibliographic record
Abstract
Anode water removal (AWR) is studied as a diagnostic tool in proton exchange membrane (PEM) fuel cell flooding. In this method, the stoichiometry of a dry hydrogen stream (no humidification) is increased stepwise at constant current, which establishes a water concentration gradient between the cathode and anode. As the anode stoichiometry is increased (in the range of 1.5–15), the anode removes more water, and the corresponding gain in voltage is measured along with the anode and cathode pressure drops. This method can be used to determine what the maximum voltage of a fuel cell is in the absence of cathode GDL and catalyst layer mass transport limitations due to liquid water. This study focused on GDLs with differing wetting properties, the inclusion/exclusion of a microporous layer (MPL), and thickness. GDLs without an MPL are more prone to flooding and show a large voltage gain (70 mV) through AWR. The effect of current density and cathode stoichiometry are also studied. Lower current densities do not produce as much water electrochemically and thus do not saturate the cathode GDL as much, leading to less gain in voltage during the AWR process. The AWR voltage gain diminishes with increasing cathode stoichiometry (1.5, 2, 4), since more water can be removed convectively from the cathode at higher air flows. Exacerbated cathode GDL flooding conditions are also studied to determine the extent to which AWR can mitigate flooding. This was accomplished via multiple GDLs on the cathode side and external water injection into the cathode flow field. In each case, the GDL saturation increases, which lowers the initial voltage. The AWR process is able to substantially increase the voltage in both cases. Thus, AWR is a useful and efficient method to observe how different fuel cell components, particularly various GDL structures, influence the cell performance due to water related mass transport losses.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".