Development of a Cathode Electrode Model Using the ORR Dual-Trap Intrinsic Kinetic Model
Bibliographic record
Abstract
A major challenge facing PEM fuel cell researchers is the development of a cathode electrode model that accurately accounts for the oxygen reduction reaction (ORR) kinetics. Rotating disk electrode (RDE) and micro-electrode experiments consistently show a doubling of the Tafel slope at a cell potential of approx. 0.75V. However, current mathematical models repeatedly use a Tafel kinetic model with constant Tafel slope, which may lead to inaccurate results. Recently, Wang et al. proposed an intrinsic, kinetic model that assumes the ORR is comprised of four intermediate steps and two intermediate adsorbed species, with the free energies of adsorption and activation used as the kinetic parameters. This model is capable of predicting the doubling of the Tafel slope and the coverage of the assumed intermediate species. The model is extended to include changes in the oxygen concentration with changing overpotential and is then implemented in an in-house two dimensional, cathode model. Results show the doubling of the Tafel slope, resulting in significant losses in the low potential region compared to Tafel kinetics. Also shown is the site blocking effect of the intermediate species, in particular Pt oxide, which completely covers the platinum surface on the catalyst layer in the presence of high oxygen concentration, i.e. at low overpotentials. Increasing the cell potential favors the reductive transition intermediate step, which consumes the Pt oxide and subsequently increases the available platinum surface. The change in Tafel slope is caused by this transition from a completely oxide covered surface to an oxide free surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".