(Invited) Imaging Techniques for Investigating Multiphase Transport Behaviour in Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
The hydrogen polymer electrolyte membrane (PEM) fuel cell provides enormous potential for a future with clean energy. However, due to cost and inefficiency barriers, PEM fuel cells have not yet reached widespread commercial adoption. Mass transport limitations arising from liquid water flooding in low temperature PEM fuel cells and acid leaching in high temperature PEM fuel cells leads to inefficiencies. If these issues become resolved, smaller and more reliable devices could be produced at a lower cost. Mass transport limitations can be minimized through the development of optimized materials, which have tailored pore structures, connectivities, conductivities, and surface wettabilities. The porous materials in PEM fuel cells could be customized for mass transport with detailed information about their structure and the dominating mass transport mechanisms that result from these structures. In this talk, X-ray based visualization techniques, such as microcomputed tomography, synchrotron X-ray radiography, and neutron radiography, for investigating multiphase transport in PEM fuel cells will be discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".