The Role of Thermal Conductivity on Liquid Water Distribution in GDLs
Bibliographic record
Abstract
Operating polymer electrolyte fuel cells (PEFCs) at increasingly higher current density and efficiency necessitates overall improvements in fuel cell water management [1]. A crucial role facilitating these improvements is played by the gas diffusion layer (GDL) within the membrane electrode assembly (MEA) [2]. The GDL is responsible for distributing the reactant gases from the flow field towards the catalyst layer as well as removing reaction products from the catalyst layer to the flow fields. For example, at the cathode, the electrochemical reaction of oxygen ions with protons and excess electrons yields water and heat, which must be removed through the GDL to the flow field. The removal of water through the GDL can lead to blocked pores in the GDL [2], thereby restricting the reactants’ gas flow towards the catalyst layer causing mass transport losses. GDL design is crucial towards improving the performance of PEFCs, especially at high current densities, such that it minimizes these transport losses. Additionally, removal of the excess heat generated at the cathode catalyst layer through the GDL indicates that the GDL thermal conductivity (k) plays a crucial role affecting the temperature distribution within the MEA [3]. In this work, two GDLs varying in thermal conductivity were selected and assembled in MEAs which were then imaged in-operando using X-ray tomographic microscopy [4]. In-Operando imaging allowed the evaluation of the liquid water distribution at steady state over a range of operational conditions such as temperature and current density. Water distribution results for a range of current densities will be presented for selected cell temperatures between 40 and 70 °C. For GDL I with the lower thermal conductivity, the channels are completely dry at 40 °C. For GDL II with the higher thermal conductivity, a wet-dry transition was observed in between 50 and 70°C. High saturations were observed in both the channel and the land regions at 50 °C, while at 70 °C the channels appear to be dry with liquid water only being present under the lands (see Figure 1). These results provide experimental evidence of a major influence of GDL thermal conductivity on liquid water distribution and overall water management in fuel cells. 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, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Canada Research Chairs. References [1] Y. Nagai, J. Eller, T. Hatanaka, S. Yamaguchi, S. Kato, A. Kato, F. Marone, H. Xu and F. N. Buechi, "Improving water management in fuel cells through microporous layer modifications: Fast operando tomographic imaging of liquid water," Journal of Power Sources, vol. 435, 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] 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. [4] 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. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".