Visualizing Water Desaturation in Frozen Gas Diffusion Layers With Flow Field Segmentation via Synchrotron X-Ray Radiography
Bibliographic record
Abstract
Synchrotron X-ray tomography images were used to study dynamic, regional water transfer behavior in the gas diffusion layer (GDL) during thawing and desaturation processes. Initially saturated, frozen GDLs were thawed and desaturated with air in a serpentine gas flow channel. On-the-fly (OTF) high speed CT scans via synchrotron X-ray allowed the capture of consecutive water transfer inside the GDL under the cold start-up gas purging condition. Desaturation data of Sigracet 35AA GDLs with three superficial gas velocities (2.88–5.98 m/s) were selected for analysis. Multiple spatial segmentation levels based on the flow field geometry, including channel vs. rib, individual channels and ribs, and smaller sections in each channel and rib, were applied to the in-plane direction to study the GDL regional thawing and desaturation behaviors. Each segmentation volume had a similar desaturation pattern in general; however, water distribution and desaturation show heterogeneity over the GDL domain, as well as relation with factors including the flow field geometry, air traveling distance, and initial saturation level. These data from the segmentation analysis expand the knowledge of localized water transfer behavior during the cold start thawing process. These data can also provide valuable information for future cold start modeling and help in optimizing the PEM fuel cell flow field design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".