The Impacts of Microporous Layer Degradation on Liquid Water Distributions in Polymer Electrolyte Membrane Fuel Cells Using Synchrotron Imaging
Bibliographic record
Abstract
The polymer electrolyte membrane (PEM) fuel cell, featuring high power density and zero-local emissions, is a favorable candidate for replacing the traditional internal combustion engine in automotive vehicles. The gas diffusion layer (GDL) in the PEM fuel cell facilitates water management and transport of reactant gases. The GDL is typically coated with a microporous layer (MPL) to improve water management and minimize contact resistance at the catalyst layer/GDL interface, further enhancing overall cell performance (1, 2). Achieving long-term durability is one of the major obstacles to the realization of commercial success for PEM fuel cells. The majority of PEM fuel cell durability research has been focused on the impacts of catalyst layer and membrane degradation on the cell performance, rather than the GDL. However, high quantities of liquid water accumulation in the GDL were reported in a fuel cell with aged (degraded) GDLs compared to a fuel cell with pristine GDLs, leading to significant performance losses through increased mass transport resistance (3). Furthermore, the MPL experienced up to twice the mass loss from the carbon substrate attributed to a carbon-corrosion based accelerated degradation protocol (4). Therefore, a clear understanding of the effects of MPL degradation on the liquid water behavior via in operando visualization is essential. In this study, as-received pristine GDLs were immersed into a heated hydrogen peroxide solution to perform an accelerated degradation process. The porosity distributions of pristine and degraded GDLs were characterized using desktop micro-computed tomography. Pristine and degraded GDLs were then assembled identically in a customized fuel cell for performance comparison and in operando liquid water visualizations conducted using synchrotron X-ray radiography. Synchrotron X-ray radiography has been demonstrated as a highly precise and accurate tool for in operando visualizing liquid water distribution in PEM fuel cells (5). It was found that the presence of the MPL was beneficial for water management in the GDL, further enhancing the cell performance. However, the MPL itself was more prone to degradation (compared to the substrate), leading to significant performance losses. Figure 1 presents the experimental through-plane liquid water distributions in pristine and degraded Sigracet 29 BC GDLs at an operating current density of 2.0 A/cm2. The leftmost location (x = 0 μm) represents the through-plane position of the catalyst layer/MPL interface and the rightmost location (x= 162.5 μm) represents the through-plane position of GDL/flow field interface. Degradation of the MPL resulted in higher quantities of liquid water in the MPL region, as well as in the carbon substrate region. The impacts of MPL degradation on liquid water behavior will be presented in this work, further highlighting impacts of degradation on liquid water management in long-term fuel cell operation. Reference: 1. J. Lee, J. Hinebaugh and A. Bazylak, J.Power Sources., 227(2013). 2. I. Zenyuk, E. Kumbur and S. Litster, J.Power Sources., 241(2013). 3. H. Liu, M. G. George, N. Ge, R. Banerjee, S. Chevalier, J. Lee, P. Shrestha, D. Muirhead, J. Hinebaugh, R. Zeis, M. Messerschmidt, J. Scholta and A. Bazylak, ECS Trans., 75,14(2016). 4. Liu H., George M.G., Messerschmidt M., Zeis R., Kramer D., Scholta J. and A. Bazylak, Journal of the Electrochemical Society.,(Submitted). 5. S. Chevalier, N. Ge, J. Lee, R. Banerjee, H. Liu, G. George, P. Shrestha, D. Muirhead, J. Hinebaugh and Y. Tabuchi, J.Power Sources., (2016). Figure 1
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".