Calibrating the X-ray attenuation of liquid water and correcting sample movement artefacts during<i>in operando</i>synchrotron X-ray radiographic imaging of polymer electrolyte membrane fuel cells
Bibliographic record
Abstract
Synchrotron X-ray radiography, due to its high temporal and spatial resolutions, provides a valuable means for understanding the in operando water transport behaviour in polymer electrolyte membrane fuel cells. The purpose of this study is to address the specific artefact of imaging sample movement, which poses a significant challenge to synchrotron-based imaging for fuel cell diagnostics. Specifically, the impact of the micrometer-scale movement of the sample was determined, and a correction methodology was developed. At a photon energy level of 20 keV, a maximum movement of 7.5 µm resulted in a false water thickness of 0.93 cm (9% higher than the maximum amount of water that the experimental apparatus could physically contain). This artefact was corrected by image translations based on the relationship between the false water thickness value and the distance moved by the sample. The implementation of this correction method led to a significant reduction in false water thickness (to ∼0.04 cm). Furthermore, to account for inaccuracies in pixel intensities due to the scattering effect and higher harmonics, a calibration technique was introduced for the liquid water X-ray attenuation coefficient, which was found to be 0.657 ± 0.023 cm(-1) at 20 keV. The work presented in this paper provides valuable tools for artefact compensation and accuracy improvements for dynamic synchrotron X-ray imaging of fuel cells.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".