Molecular Dynamics Study of Reaction Conditions at Active Catalyst-Ionomer Interfaces in Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Understanding the local reaction conditions at the catalyst-ionomer interfaces inside of polymer electrolyte fuel cells is vital for improving cell performance and stability. Properties of the water film and distributions of protons and oxygen molecules at the catalyst-ionomer interface are affected by the state of the catalyst and support surfaces and the structure of the ionomer skin layer. In this work, the interfacial region between catalyst and support surface and ionomer skin is simulated using molecular dynamics. This water-filled nanopore model is constructed to study the impact of local charge density, density of sidechains at the ionomer layer, and water layer thickness on the water structure and electrostatic conditions in the pore as well as the transport properties of water, hydronium, and molecular oxygen at the interface. The analysis of the flooded pore model indicates that surface hydrophilicity, represented by water adsorption and the formation of an ordered water layer at the surface, is a major factor determining the interfacial proton density, ionomer sidechain mobility, and interfacial oxygen transport resistance. The results obtained can guide the design of new catalyst materials, where the hydrophilicity of the surface can be tailored to minimize the local proton transport resistance and improve electrode performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".