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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".