Microstructural Analysis of Electrode Performance in Fuel Cells at Varying Water Contents
Bibliographic record
Abstract
A novel nucleation based water intrusion algorithm is used to simulate liquid water accumulation in a catalyst layer (CL) microstructure. The algorithm is based on a clustered full morphology model that tracks the liquid water propagation in the CL. A numerical electrode model capable of simulating proton conduction in the ionomer and oxygen transport in the ionomer, gas filled pores and liquid filled pores along with electrochemical reaction at the ionomer-solid interface is used to simulate the electrochemical reactions in the partially saturated CLs at different saturations obtained from the water intrusion algorithm. Simulations on a representative elementary volume of a CL show that the local saturation does not have a significant effect on the current density due to small diffusion length. Analysis of electrochemical performance on a full, 1.8 μm, through-plane cross-section of the CL shows that liquid water accumulation results in mass transport losses of nearly 12% at a saturation of 59.7% even at low volumetric current densities of 4599 A/cm 3 . The results from the current simulations indicate that a representative elementary volume analysis of the electrochemical performance of the CL at different saturations might not provide insight into the pore-scale electrochemical reactions and full CL simulations might be needed to describe the effects of local flooding in the CL.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".