(Plenary) Advanced Visualization Tools to Investigate PEM Fuel Cell Materials
Bibliographic record
Abstract
Significant advancements have been made towards the commercialization of polymer electrolyte membrane (PEM) fuel cells in recent years. However, prohibitive costs and limited durability still remain as barriers toward deep market penetration, and these challenges largely stem from an incomplete understanding of how the nanoscale and microscale features of the porous materials in the PEM fuel cell affect multiphase and thermal transport within the bulk and at the interfacial regions of these porous layers. Non-traditional tools are needed more than ever to address these challenges. In the Thermofluids for Energy and Advanced Materials (TEAM) Laboratory at the University of Toronto, we employ a variety of advanced visualization tools to characterize the nanoscale and microscale features of the porous layers of the PEM fuel cell within ex-situ and in-situ environments. Accurate material characterizations are used as inputs for developing our predictive models, and visualizations serve as validations for our numerical modelling work. Our goal is to develop predictive numerical models that will serve as design tools that we can use to prescribe three-dimensional material properties for tailored thermal and liquid water transport for the next generation of PEM fuel cell technologies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.015 |
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".