Nanoscale X-ray tomographic imaging of liquid water in fuel cell electrode materials
Bibliographic record
Abstract
Efficient transport of water in a polymer electrolyte membrane fuel cell (PEMFC) is one of the key factors to achieve a high performance with balanced cell humidity and transport of gas reactants. Micro-scale 3D X-ray computed tomography (XCT) has shown great promise in understanding and visualizing liquid water distribution and transport; although, mainly within the macroscopic gas diffusion layer (GDL) substrate. The objective of the present work is to extend XCT based liquid water visualization to the nano-scale pores of the microporous and catalyst layers (MPLs and CLs) in PEMFCs. A custom methodology of water distribution visualization in MPL and CL at 100 % relative humidity (RH) condition is developed using a lab-based nano-resolution XCT (NXCT) system. The design of a custom-built X-ray transparent fixture for capturing water domains along with the imaging procedure, constraints, and challenges are discussed. Utilization of an in-house built fixture coupled with Zernike phase-contrast imaging mode, has allowed for the quantification and visualization of ∼ 18 % and ∼ 6 % of water volume fraction in wetted MPL and CL, respectively. The methodology discussed here is a step forward to understand the water distribution in nano-porous media and can be further modified to be translated to real working conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".