Predicting Membrane Lifetime with Cerium Oxide in Heavy Duty Fuel Cell Systems
Bibliographic record
Abstract
Stringent reliability and durability requirements for fuel cells in heavy duty vehicles demand highly durable ionomer membranes.Fuel cell membranes degrade chemically and mechanically during operation, which can lead to membrane thinning, pinhole and crack formation and eventual failure due to hydrogen leaks.The chemical portion of degradation can be suppressed with the use of radical scavenging agents such as cerium oxide.In order to implement extended durability solutions in actual field operation, however, aptly designed accelerated durability tests and empirical models are needed to predict membrane lifetime under various operating conditions, while also considering additive stability over time.Here, an empirical membrane lifetime model recently developed for transit bus applications is modified and demonstrated to predict membrane lifetime with cerium oxide incorporated into the membrane electrode assembly as a chemical stabilizer.The lifetime prediction approach utilizes laboratory scale experimental data from an accelerated membrane durability test complemented by measured cerium washout rates.Provided that the cerium washout rates were relatively low, the predicted membrane lifetime of cerium supported membranes was found to significantly exceed the ultimate 25,000 h heavy duty durability target.
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 | 0.000 | 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 | 0.001 | 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".