Physical-Statistical Modeling and Analyses of Catalyst Degradation in PEM Fuel Cells
Bibliographic record
Abstract
Platinum durability in the catalyst layers of polymer electrolyte fuel cells is a major challenge that delays the full commercialization of fuel cell vehicles. This work presents a physical-statistical model of platinum degradation. The modeling framework of the Pt degradation model encompasses the main processes at the particle level, namely catalyst dissolution, redeposition, coagulation, and detachment, and it accounts for the effluence of Pt ions into the polymer electrolyte membrane. Data sets analyzed with the model encompass information on changes in electrochemically active surface area, particle radius distribution, Pt mass distribution, and thickness of the catalyst layer. A systematic algorithm is developed to process experimental inputs and generate output information on kinetic rate parameters. The model-based data treatment proceeds in two stages: (i) statistical exploration of the complete parameter space using Monte Carlo techniques and (ii) an optimization routine to deconvolute contributions to degradation by different mechanisms and determination of the pertinent set of parameters. Model outcomes contribute to fuel cell performance and lifetime evaluation, development of strategies for degradation mitigation, and calibration of efforts in design and fabrication of catalyst materials and catalyst layers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".