Bibliographic record
Abstract
The cathode catalyst layer (CCL) is the major competitive ground for electrochemical reaction, reactant transport, and water and heat exchange in a polymer electrolyte fuel cell (PEFC). Nevertheless, it is often treated as a thin interface. Its pivotal role in the fuel cell water balance is unexplored. Here, the structural picture of CCLs forms the basis for a novel model that links spatial distributions of processes with water handling capabilities and current voltage performance. In the first step, the statistical theory of random composite media is used to relate composition, porous structure, wetting properties, and partial saturation to effective properties. In the second step, these effective properties are used in a macrohomogeneous model of CCL performance. A set of reasonable simplifications leads to a full analytical solution. Results demonstrate that the CCL acts like a watershed in the fuel cell, regulating the balance of opposite water fluxes toward membrane and cathode outlet. Due to a benign porous structure, the CCL represents the prime component for the conversion of liquid to vapor fluxes in PEFCs. Furthermore, the CCL is highlighted as a critical component in view of excessive flooding that could give rise to limiting current behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".