Insights into structure and properties of catalyst-ionomer interfaces in a PEM fuel cell cathode from atomistic molecular dynamics simulations
Bibliographic record
Abstract
The structure and properties of the catalyst-ionomer interface at the cathode exert a major impact on the performance of polymer electrolyte membrane fuel cells. The interface is affected by both the chemical structure of the ionomer and the potential-dependent changes to the catalyst/support surface during operation. This work presents molecular dynamics simulations of the catalyst-ionomer interface for an expanded Pt/C-ionomer thin film model. Simulations reveal that the structure of the ionomer film is sensitive to the oxidation state of the carbon support, with the preferential ionomer orientation shifting from backbone-towards-carbon to sidechain-towards-carbon oxide. The equivalent weight of the ionomer is shown to determine ionomer packing at the catalyst surface, which could impact the local oxygen transport resistance. The equivalent weight also influences the local proton concentration (or pH) and the proton conductivity at the catalyst-ionomer interface. Shorter sidechain length also increases conductivity by forming larger water clusters that act as channels for hydronium mobility. Overall, the presented simulations demonstrate how the ionomer composition could be tuned to enhance performance via its impact on kinetic, ohmic, and transport losses in fuel cell voltage.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".