Probing Heterogeneous Water Distributions within Fuel Cell Membranes Using Combined Neutron and X-Ray Tomography (NeXT)
Bibliographic record
Abstract
Water management in a fuel cell is an essential prerequisite for achieving high cell performance, where sufficient water is required for membrane hydration while excess liquid water leads to undesired mass transport losses. Existing heterogeneity within the fuel cell, such as those created by flow-field lands and channels create three-dimensional (3-D) heterogeneity in water distribution within the fuel cell components, which may have long term effect on the durability of fuel cell components, such as the membrane (1). 3-D visualization techniques such as X-ray (1-4) and neutron (5) tomography are powerful in revealing the effect of the existing heterogeneity on water distributions in 3-D. However, there is an opportunity to combine neutron and X-ray tomography (6) to probe into the details of these heterogenous water distributions even further and clarify their impacts on 3-D membrane hydration. In this study, we investigate the effect of heterogeneity in fuel cells (primarily land-channel heterogeneity) on 3-D membrane hydration and membrane morphology changes during fuel cell operation using simultaneous neutron and X-ray tomography (NeXT). The fuel cell is tested at varying gas humidity conditions in a serpentine flow-field configuration. A simultaneous coupling of neutron and X-ray imaging provides high contrast across various components of the fuel cell. Specifically, neutrons are highly attenuated by hydrogen atoms; hence neutron imaging is used to accurately locate and quantify operando water distribution. X-rays are sensitive to metals; hence simultaneous X-rays imaging is used to track metal-containing components (metal flow-field) and interfaces (such as interface between Pt-containing catalyst layer and membrane). This study demonstrates how heterogeneity in fuel cells plays a role in 3-D membrane hydration and needs to be tailored to enhance cell performance. References Y. Singh, R. T. White, M. Najm, T. Haddow, V. Pan, F. P. Orfino, M. Dutta, and E. Kjeang, J. Power Sources., 412 (2019): 224-237. Y. Nagai, J. Eller, T. Hatanaka, S. Yamaguchi, S. Kato, A. Kato, F. Marone, H. Xu and F. N. Büchi, J. Power Sources. , 435 (2019). S. J. Normile, D. C. Sabarirajan, O. Calzada, V. De Andrade, X. Xiao, P. Mandal, D. Y. Parkinson, A. Serov, P. Atanassov and I. V. Zenyuk, Meter. Today Energy. , 9 (2018). S. S. Alrwashdeh, I. Manke, H. Markötter, M. Klages, M. Göbel, J. Haußmann, J. Scholta and J. Banhart, ACS Nano. , 11, 6 (2017). J. M. LaManna, Y. Yue, T. A. Trabold, J. D. Fairweather, D. S. Hussey, E. Baltic and D. L. Jacobson, Meet. Abstr. - Electrochem. Soc. , 32 (2017). J. M. LaManna, D. S. Hussey, E. Baltic and D. L. Jacobson, Rev. Sci. Instrum. , 88, 11 (2017).
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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 | 0.001 | 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".