Accelerated Testing of Carbon Corrosion and Membrane Degradation in PEM Fuel Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accelerated Stress Tests (ASTs) to characterize carbon corrosion were performed on MEAs based on 3 different carbon supports. High surface area carbon exhibited the best initial performance but the fastest degradation rate. On the other hand, highly graphitized carbon exhibiting the slowest degradation rate but had the lowest initial performance. TEM analysis of the MEAs after corrosion indicated Pt particle size growth in all the catalyst layers in addition to significant thinning of the high surface area carbon-based catalyst layers. Voltage loss breakdown identified mass transport losses resulting from a compaction of the catalyst layer porosity as the greatest contributor to performance loss. Three different membrane ASTs were performed on 2 distinct MEAs (designated P5 and HD6) from Ballard Power Systems and the degradation compared to that observed in the field. The membrane chemical degradation AST resulted in significant membrane thinning not observed in the field. The membrane mechanical degradation AST was able to reproduce the degradation phenomenon observed in the field but had little ability to distinguish between various membranes. A combined mechanical/chemical AST was examined to better simulate the degradation rates observed in the field.
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.
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) | 0.000 | 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 it