Effect of CeOx Crystallite Size on the Chemical Stability of CeOx Nanoparticles
Bibliographic record
Abstract
CeOx is an excellent free radical scavenger to improve polymer electrolyte membrane durability.However, this metal oxide will dissolve during accelerated stress testing (AST), with the resulting cations transporting to the cathode catalyst layer (CCL) leading to performance reduction/degradation of the PEMFC.Controlling the rate of CeOx dissolution is therefore of great importance, as it may be possible to maintain sufficient Ce cations for free radical scavenging while minimizing the impact of these cations on the CCL.Here the effect of CeOx crystallite size on CeOx dissolution was investigated.Three CeOx additives were prepared having crystallite sizes of 6, 13, or 25 nm.An ex-situ method was used to evaluate the chemical stability of these three CeOx samples, as well as one commercially available CeOx.It was determined that surface area, rather than crystallite size, is the best predictor of chemical stability.In-situ membrane electrode assembly AST cycling was then performed, demonstrating that when low loadings of CeOx (0.006 mg/cm 2 ) are used, the ex-situ method correctly predicts trends in end of life (EOL) performance.Finally, it is shown that increasing the anode RH during AST cycling leads to significantly higher EOL performance losses.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".