Optimization of Membrane Electrode Assemblies for PEMFC
Bibliographic record
Abstract
In the last decade, increasing concerns about global warming, air pollution in largely populated areas and energy security have emerged due to the dependance of the automotive and energy sectors on fossil fuels. In response to these concerns, fuel cells and, in particular, proton exchange membrane fuel cells (PEMFC) have emerged as a good candidate to replace the current fossil fueled energy conversion devices such as the internal combustion engines because of its ability to run on non-hydrocarbon based fuels and to power a vehicle producing only water vapor emissions. The success of PEMFC as the next energy conversion device will depend on the advances made in the next decade in PEMFC design and, therefore, much research in this area is needed. However, PEMFC design is not simple because their performance depends on a large number of coupled physical phenomena such as fluid flow, heat, mass and charge transport and electrochemistry. These coupled processes are controlled by a large number of physical ∗PhD Candidate, Institute of Integrated Energy Systems and Mechanical Engineering Department, secanell@uvic.ca, AIAA Student Member †Professor, Institute of Integrated Energy Systems and Mechanical Engineering Department, ndjilali@uvic.ca ‡Professor, Mechanical Engineering Department, suleman@uvic.ca, AIAA Associate Fellow
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".