Microstructure-Based Modeling of Aging Mechanisms in Catalyst Layers of Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
This work is comprised of a versatile multiscale modeling of carbon corrosion processes in catalyst layers (CLs) of polymer electrolyte fuel cells (PEFCs). Slow rates of electrocatalytic processes in CLs and materials aging are the main sources of voltage loss in PEFCs under realistic operating conditions. We combined microstructure data obtained from coarse-grained molecular dynamics (CGMD) simulations with a detailed description of the nanoscale elementary kinetic processes and electrochemical double-layer effects at the catalyst/electrolyte and carbon/electrolyte interfaces. We exclusively focused on morphology and microstructure changes in the catalyst layer of PEFCs as a result of carbon corrosion. By employing extensive CGMD simulations, we analyzed the microstructure of CLs as a function of carbon loss and in view of ionomer and water morphology, water and ionomer coverage, and overall changes in carbon surface. These ingredients are integrated into a kinetic model, which allows capture of the impact of the structural changes on the PEFC performance decay. In principle, such multiscale simulation studies allow a relation of the aging of CLs to the selection of carbon particles (sizes and wettability), the catalyst loading, and the level of ionomer structural changes during the CL degradation 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".