Determining Substrate Oxygen Transport Resistance at Limiting Current Using Pore Network Modelling
Bibliographic record
Abstract
The polymer electrolyte membrane (PEM) fuel is a promising technology to supplant traditional, greenhouse gas emitting energy conversion devices. However, widespread commercial adoption of the PEM fuel cell is hindered by the high cost of the precious metals required for its catalyst (1). The amount of catalyst required in a PEM fuel cell can be reduced if the reactant (oxygen) is delivered more efficiently to the catalyst. Therefore, previous studies have sought to characterize the mechanisms affecting oxygen transport in PEM fuel cells (2)(3)(4)(5). In past studies, the oxygen transport resistance of a PEM fuel cell was measured at limiting current, and this value was further decomposed into contributions from individual PEM fuel components using empirical modelling. By resolving each PEM fuel cell component’s contribution to oxygen transport resistance, an informed approach can be undertaken to design next generation components with enhanced oxygen transport capabilities. In this study, we employed pore network modelling to quantify the oxygen transport resistance arising from the presence of liquid water within the substrate region of the gas diffusion layer (GDL). First, we captured the operando liquid water distribution within a PEM fuel cell operating at limiting current using synchrotron X-ray radiography. Next, we used a combination of invasion percolation and an in-house water invasion inlet selection algorithm to partially saturate the representative pore network of the substrate and to reproduce the mean water saturation. Finally, we obtained substrate oxygen transport resistance values by performing transport simulations on the partially saturated pore network and compare our findings with the experimental observations. References X. X. Wang, M. T. Swihart, and G. Wu, Nature catalysis, 2, 7 (2019). D. R. Baker, D. A. Caulk, K. C. Neyerlin, and M. W. Murphy, J. Electrochem. Soc., 156, B991 (2009). T. Reshtenko, J. St-Pierre, J. Electrochem. Soc., 161, F1089 (2014) D. Muirhead, R. Banerjee, M. G. George, N. Ge, P. Shrestha, H. Liu, J. Lee, A. Bazylak, Electrochimica Acta, 274 (2018). N. Ge, P. Shrestha, M. Balakrishnan, D. Ouellette, A. K. C. Wong, H. Liu, C. H. Lee, J. K. Lee, A. Bazylak, Electrochimica acta, 328 (2019).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".