Numerical investigation of transport phenomena and electrochemical reactions in PEM fuel cell cathode
Bibliographic record
Abstract
Cathode over potential represents the single largest cell voltage loss mechanism in PEM fuel cells. The loss is mainly attributed to the slow nature of oxygen transport and sluggish electrochemical kinetics. These form the focus of the present study. The cathode catalyst layer is assumed to be composed of a uniform distribution of catalyst, liquid water, electrolyte, and void space. A serpentine flow field is used to distribute the oxidant over the active cathode electrode surface, with pressure loss in the flow direction along the channel. Both the convection and diffusion process occur in the electrode backing layer and the catalyst layer. The Stefan-Maxwell equation is used to model the multi-species diffusion. The two-dimensional numerical simulation highlights the transport process of oxygen, electron and proton in the catalyst layer, and their impact on the electrochemical process and the current density distribution. It is found that electron transfer to the reaction site leads to more cell losses than proton transfer. Most of the losses from electron transfer occur in the bipolar plate and backing layer. Thus, efforts should be focused on the improvement of those two domains. In addition, the assumption of water being in the vapour form everywhere cannot hold when the inlet relative humidity is high. Therefore, modeling liquid water is essential for a better understanding of the electrochemical process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".