Model‐Based <i>Ex Situ</i> Diagnostics of Water Fluxes in Catalyst Layers of Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Abstract The ability to predict the electrochemical performance of the cathode catalyst layer in a polymer electrolyte fuel cell hinges on a precise knowledge of water distribution and fluxes. Water transport mechanisms that must be accounted for include vapor diffusion, liquid water permeation and vaporization exchange. In order to facilitate experimental efforts to this effect, we propose an ex situ model of water fluxes in catalyst layers. The model formulation is similar to transmission line models that are widely used in the analysis of electrochemical impedance spectra of porous composite electrodes. Focusing in this article on steady state and isothermal conditions, we rationalize the response function between defined environmental conditions, i.e. gas pressures, partial vapor pressures and temperature, which are defined at the boundaries of the catalyst layer, and the net water flux. This response function provides diagnostic capabilities to isolate and extract water transport parameters of catalyst layers from measurements of water fluxes through membrane electrode assemblies or half cell systems. An important asset of the model is the ability to analyze catalyst layer transport properties under partial saturation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".