X-ray Computed Tomography Visualization of Liquid Water in Proton Exchange Membrane Fuel Cells: A State-of-the-Art Review
Bibliographic record
Abstract
Abstract Liquid water visualization in an operating fuel cell is a sophisticated research field that integrates interdisciplinary expertise in engineering, physics, optics, chemistry, materials science, imaging, image processing, and computational modeling. Advances in visualization techniques, such as X-ray computed tomography (XCT), have enabled observations of water transport and distribution inside proton exchange membrane fuel cells (PEMFCs) under in situ/operando conditions. Combined with electrochemical characterization and modeling tools, this approach can improve our understanding of the fundamental mechanisms of the dynamic water behavior and their impact on the cell performance and durability based on the structure–property–function relationships within the PEMFC core components. This article presents a concise review of the state-of-the-art literature featuring XCT methodology for water visualization in PEMFCs, including the flow field and all parts of the membrane electrode assembly. It synthesizes the key current trends and highlights the challenges and future research opportunities in the field, contributing to the development of high-power-density, durable, and cost-effective PEMFC technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".