Combining Pore-Scale Liquid Water Visualization and Modeling to Understand Water Transport in Operating Fuel Cells
Bibliographic record
Abstract
An in-depth understanding of liquid water transport in fuel cell gas diffusion layers (GDLs) has the potential to inform the development of novel water management strategies that lead to improved fuel cell performance at high current densities. Several visualization (1-4) and modeling (5, 6) studies have shed light into some of these mechanisms of liquid water transport. However, due to the heterogenous nature of porous materials within the fuel cells, pore-scale modeling and simulations are required for in-depth analysis. Due to recent advancements in visualization techniques, particularly high-resolution synchrotron X-ray (1-4) and neutron (7) micro-computed tomography, the spatial distribution of liquid water within individual GDL pores during fuel cell operation can be resolved. In addition, pore network modeling (8) has been used to simulate and examine pore-to-pore water transport behavior within a wide range of GDL materials. However, we need to combine pore-network simulations with experimental data of realistic fuel cell conditions to study water transport in representative operating conditions in greater detail. In this study, we combined 3-D computed tomography and pore network modeling to expand on our understanding of liquid water transport within gas diffusion layers of operating fuel cells. First, we used synchrotron X-ray tomography to examine the micron-scale GDL porous structure and in-operando 3-D liquid water distribution using a custom fuel cell specialized for imaging. Then, we simulated water transport within the examined GDL structure using pore network modelling. We then compared the 3-D experimental results to our simulations to gain a deeper understanding of realistic inlet conditions for liquid water transport within the GDL. The study demonstrates a robust strategy to probe, understand, and predict liquid water transport within the complex porous structures in PEM fuel cells. References 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). J. Eller, J. Roth, F. Marone, M. Stampanoni and F. N. Büchi, J. Electrochem. Soc., 164, 2 (2017). S. S. Alrwashdeh, I. Manke, H. Markötter, M. Klages, M. Göbel, J. Haußmann, J. Scholta and J. Banhart, ACS Nano., 11, 6 (2017). P. Krüger, H. Markötter, J. Haußmann, M. Klages, T. Arlt, J. Banhart, C. Hartnig, I. Manke and J. Scholta, J.Power Sources., 196, 12 (2011). C. Y. Wang, Chem. Rev., 104 (2004). P. P. Mukherjee, Q. J. Kang and C. Y. Wang, Energy Environ. Sci., 4, 2 (2011). 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. Gostick, M. Aghighi, J. Hinebaugh, T. Tranter, M. A. Hoeh, H. Day, B. Spellacy, M. H. Sharqawy, A. Bazylak, A. Burns, W. Lehnert and A. Putz, Comput. Sci. Eng., 18, 4 (2016).
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".