Physical Theory of Platinum Nanoparticle Dissolution in Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
The loss of electrochemically active surface area (ECSA) causes severe performance degradation over relevant lifetimes of polymer electrolyte fuel cells. Using a simple physical model, we analyze the interrelations between kinetics of platinum nanoparticle dissolution, evolution of the particle size distribution, and ECSA loss with time. The model incorporates the initial particle radius distribution, and it accounts for kinetic processes involving Pt dissolution, Pt−O formation, and Pt−O dissolution. Employing reasonable simplifying assumptions to the governing equations, a full analytical solution was found under potentiostatic conditions. The simplified model predicts the evolution of the particle radius distribution as well as ECSA loss with time, in close agreement with experimental ex situ and in situ studies. The study indicates that the rates of chemical Pt−O dissolution, driven by the particle size dependence of the cohesive energy, may dominate over electrochemical dissolution. Fitting of the model to experimental data provides an effective surface tension and an effective rate constant of Pt−O dissolution. Implications of the model for the development of strategies to reduce ECSA loss are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".