(Invited) Imaging and Quantitative Chemical Mapping of PEM-FC Catalyst Layers By Scanning Transmission X-Ray Microscopy
Bibliographic record
Abstract
Low temperature, hydrogen-fueled, proton exchange membrane fuel cell (PEM-FC) based engines are being developed rapidly for near-term implementation in mass production, personal automobiles. Materials and process research aiming to further optimize these systems is focused on understanding and controlling various degradation processes (carbon corrosion, Pt migration, cold start), and reducing cost by reducing or eliminating Pt in the electro-catalyst, especially for the oxygen reduction reaction (ORR), and optimizing the nanoscale distribution of the cathode components. Soft X-ray scanning transmission X-ray microscopy (STXM) [1] is a powerful tool to study PEM-FC catalyst layers (membrane electrode assemblies, MEA). STXM provides spectroscopic identification and quantitative mapping of chemical components with 30 nm spatial resolution in both 2D projection and 3D spectro-tomography. For a given radiation dose, it provides much more chemical information than analytical transmission electron microscopy (TEM) and thus it has significant advantages for mapping ionomer in MEA cathodes [2, 3], which has proven to be a very challenging component to detect by TEM due to the high sensitivity of ionomer to radiation damage. This tutorial will describe the instrumentation, methodology and data analysis involved in applying STXM to PEM-FC catalyst layers, and illustrate its capabilities with results from recent studies [4, 5]. STXM performed on BL 10ID1 at the Canadian Light Source and on BL 5.3.2.2 at the Advanced Light Source. Research supported by AFCC, NSERC, Canada Research Chairs, and the Catalyst Research for Polymer Electrolyte Fuel Cells (CaRPE-FC) network. [1] A.P. Hitchcock, Soft X-ray Imaging and Spectromicroscopy in Handbook on Nanoscopy , eds.G. Van Tendeloo, D. Van Dyck and S. J. Pennycook 2012. (Wiley) [2] D. Susac, V. Berejnov, A.P. Hitchcock, and J. Stumper, ECS Transactions 41 (2011) 629. [3] D. Susac, V. Berejnov, A.P. Hitchcock and J. Stumper, ECS Transactions 50 (2012) 405 [4] A.P. Hitchcock, et al., J. Power Sources 266 (2014) 66 [5] V. Lee, et al. J. Power Sources 263 (2014) 163 Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| 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 teacher head, 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".