A Pore-Scale Model of Oxygen Reduction in Ionomer-Free Catalyst Layers of PEFCs
Bibliographic record
Abstract
We present a model for oxygen reduction in water-filled, cylindrical nanopores with platinum walls. At one end, the pores are in contact with a polymer electrolyte membrane. The electrostatic interaction of the protons with the charged pore walls drives proton migration into the ionomer-free channels. We employ the Stern model to relate the surface charge density at the pore walls to the electrode potential. Proton and potential distributions within the pores are governed by the Poisson–Nernst–Planck theory and the oxygen distribution by Fick's law. Assuming a small local current density from oxygen reduction, we found an approximate analytical solution to the transport equations. The metal surface charge density and the corresponding proton conductivity of the pores are tuned by the deviation of the electrode potential from the potential of zero charge of the metal phase, which is the key determinant of the effectiveness of platinum utilization. Other determinants of pore performance are the Helmholtz capacitance, electrokinetic parameters, and pore size and length. Upon upscaling, the model is consistent with polarization data for ionomer-free, ultrathin catalyst layers in polymer electrolyte fuel cells (PEFCs). We discuss the implications of the model for the materials selection and nanostructural design of such catalyst layers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".