Physics-Based Modeling of Platinum Catalyst Dissolution and Oxidation in PEM Fuel Cells: A Focused Review
Bibliographic record
Abstract
The parallel electrochemical reactions of platinum dissolution and oxide formation are key reactions that govern surface area loss and subsequent catalyst degradation in polymer electrolyte membrane (PEM) fuel cells. Studying these mechanisms through physics-based approaches is critical for understanding catalyst degradation and for developing more durable fuel cells. This review summarizes advances in physics-based modeling of platinum dissolution and oxidation, presenting three of the most widely used frameworks: the Darling-Meyers, Holby-Morgan, and Rinaldo-Stumper-Eikerling models. These models form the conceptual foundation for many subsequent studies on platinum dissolution and oxidation, and this review examines how recent work has expanded upon these frameworks to illustrate the conceptual evolution of physics-based degradation models. Additional models proposed in the literature are also discussed as alternative approaches that represent newer and emerging directions in modeling platinum degradation. Finally, the review compares the capabilities and limitations of existing models and highlights emerging trends and potential directions for future model development. This review aims to provide a focused guide for researchers developing next-generation physics-based catalyst degradation models for PEM fuel cells.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".