Development of a 4-in-1 Device to Measure Component Properties of Gas Diffusion Layer/Porous Transport Layer for Both Proton Exchange Membrane Fuel Cell and Water Electrolyzer
Bibliographic record
Abstract
This work aims at developing a 4-in-1 device to simultaneously measure parameters under compression (UC), including thickness (TUC), resistivity (RUC, through-plane), and in-plane permeability (IPP-UC), and to separately measure through-plane permeability (TPP). The simultaneous measurement of TUC/RUC/IPP-UC is accomplished through an annulus sample by measuring the flow rate–pressure relationship in a radial flow [1]. By adding a two-piece adaptor, TPP can also be measured using a disk sample. Typical TUC/RUC/IPP-UC/TPP curves of a gas diffusion layer (GDL) are shown in Figure 1. Upon the design requirements, the TUC/RUC/IPP-UC/TPP tester is calibrated and validated. The 4-in-1 device was originally designed for the characterization of the GDL, a key component of the membrane electrode assembly (MEA) for a proton exchange membrane (PEM) fuel cell. The GDL properties are greatly associated with the cell performance, especially under compression. To ensure the quality of GDLs, it is of great importance to characterize GDL properties with respect to the component standardization and specification. The application of the 4-in-1 device was later extended for the characterization of the porous transport layer (PTL), a crucial component for the anode of PEM water electrolyzer in responding to the increased research and efforts made in the development of, mostly, Ti-based PTLs for PEM water electrolysis [2]-[4] , as commercially available PTLs are basically materials borrowed from other applications, e.g., filtration. The developed TUC/RUC/IPP-UC/TPP device has been proven to be a useful tool for the quality control (QC) of GDLs and PTLs, facilitating the component development, complementing the component supply chain, contributing to cell reliability, and thus reduction of cost, for both the PEM fuel cell and PEM water electrolysis technologies. Acknowledgements This work is financially supported by the Office of Energy Research and Development (OERD) and the Advanced Clean Energy (ACE) Program of the National Research Council Canada (project #NRC-23-140). References [1] X.-Z. Yuan, E. Gu, R. Bredin, M. Baker, S. Lee, T. Biggs, A. Bock, V. Banhardt, J. Russell, F. Girard, Development of a 3-in-1 device to simultaneously measure properties of gas diffusion layer for the quality control of proton exchange membrane fuel cell components, J. Power Sources, 477, 30 (2020) 229009 [2] Z. Kang, S. M. Alia, J. L. Young, G. Bender, Effects of various parameters of different porous transport layers in proton exchange membrane water electrolysis, Electrochim. Acta 354 (2020) 136641 [3] R. J. Ouimet, J. L. Young, T. Schuler, G. Bender, G. M. Roberts, K. E. Ayers, Measurement of Resistance, Porosity, and Water Contact Angle of Porous Transport Layers for Low-Temperature Electrolysis Technologies, Front. Energy Res., 10 (2022) 911077. [4] J. K. Lee, G. Y. Lau, M. Sabharwal, A. Z. Weber, X. Peng, M. C. Tucker, Titanium porous-transport layers for PEM water electrolysis prepared by tape casting, J. Power Sources 559 (2023) 232606 Figure 1 Comparison of typical TUC/RUC/IPP-UC/TPP curves of commercial GDLs and PTLs: (a) TUC; (b) RUC; and (c) IPP-UC as a function of compression. (d) TPP of different samples as a function of differential pressure 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".