Fingerprints of Automotive Fuel Cell Degradation
Bibliographic record
Abstract
Durability of fuel cell components is a challenge for commercialization of automotive fuel cells. Materials such as the cathode catalyst layer are subjected to a number of voltage stresses which can cause multiple failure modes such as carbon corrosion and Pt dissolution. This work examines three degradation cycles and attempts to de-convolute the amount of degradation from carbon corrosion. A 1.4V hold, 1.0-1.4V cycle, and a 0.6-1.4V cycle were examined. Performance and EPSA measurements were taken and CO2 release was monitored over time. A carbon corrosion fingerprint was created using 1.4V hold results and used to evaluate the contribution of the corrosion mechanism to performance degradation in the other cycles. The results indicate that the 1.0-1.4V cycle degrades primarily by carbon corrosion while the 0.6-1.4V cycle had a significant contribution from other mechanisms, including Pt dissolution.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".