Effects of Selected Fuel Cell Operating Conditions on the Distribution of Liquid Water in the Cathode Gas Diffusion Layer
Bibliographic record
Abstract
Effective water management is required to enable polymer electrolyte fuel cells (PEFCs) to operate efficiently, particularly at the high current densities needed to make them relevant for transportation and stationary applications. The presence of liquid water can influence the performance of a PEFC when the rate of water production exceeds the rate of water removal [1]. More specifically, accumulation of water within the cell becomes problematic when the pores within the gas diffusion layer (GDL) become filled, resulting in restriction of the transport of reactants to the catalyst layer [2]. Identifying operational conditions that minimize mass transport losses associated with excess water accumulation is crucial for improving the performance of PEFCs, especially at high current densities. Additionally, GDL design plays an important role in reducing mass transport losses, with thermal conductivity being a key factor influencing the distribution of liquid water. In this work, miniaturized fuel cells were operated under various conditions and imaged in-operando using X-ray Computed Tomography (XCT) [3]. This imaging technique facilitates the evaluation of the distribution of liquid water at steady state over a range of operational conditions. The influence of current density on liquid water distribution was assessed at selected cell temperatures between 40°C and 70°C. The results of this study showed high saturations under both the land and the channel regions at 50 °C. However, the channels appeared to be dry at 70 °C, with liquid water present only under the lands. These data were compared to predictions generated using a one-dimensional (1-D) model of the through-plane GDL, which considers specific operating conditions and GDL characteristics, such as thermal conductivity [4]. These results demonstrate that the 1-D model can be used to predict the presence or absence of liquid water within the GDL (and, by extension, PEFC performance) under a broad range of operating conditions. Hence, the 1-D model may be useful for focusing future laboratory investigations. Keywords – operando, X-ray tomographic microscopy, GDL, water visualization Acknowledgement Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Ballard Power Systems, Simon Fraser University, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Canada Research Chairs. References [1] O. S. Ijaodola, Z. El-Hassan, E. Ogungbemi, F. N. Khatib, T. Wilberforce, J. Thompson and A. G. Olabi, "Energy efficiency improvements by investigating the water flooding management on proton exchange membrane fuel cell (PEMFC)," Energy, vol. 179, pp. 246-267, 2019. [2] H. Xu, M. Buehrer, F. Marone, T. J. Schmidt, F. N. Buechi and J. Eller, "Effects of Gas Diffusion Layer Substrates on PEFC Water Management: Part I Operando Liquid Water Saturation and Gas Diffusion Properties," Journal of The Electrochemical Society, vol. 168, 2021. [3] F. A. Aroge, B. S. Parimalam, J. A. MacDonald, F. P. Orfino, M. Dutta and E. Kjeang, "Analysing operando 2D X-ray transmission images for liquid water distribution in polymer electrolyte fuel cells," Journal of Power Sources, vol. 564, 2023. [4] D. A. Chaulk and D. R. Baker, "Heat and Water Transport in Hydrophobic Diffusion Media of PEM Fuel Cells," Journal of The Electrochemical Society, vol. 157, 2010.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".