Towards Understanding of the Anode Catalyst Layer Structure for Extended Reversal Tolerance: An Advanced Characterization Approach
Bibliographic record
Abstract
Proton exchange membrane fuel cells for automotive applications (PEMFC) frequently experience transient conditions such as start-up/shut-down, freeze start-up and local fuel starvation at the anode catalyst layer (ACL) that are responsible for the loss of performance and durability as a result of irreversible carbon corrosion [1]. To mitigate such degradation, oxygen evolution catalysts have been incorporated into ACL and studied for effects on membrane electrode assembly (MEA) performance and reversal tolerance. Ru and Ir oxides are among the most studied catalysts for oxygen evolution reaction (OER) in both PEMFCs and water electrolyzers [2-5]. Although comparative measurements have been performed to investigate the OER activity trends, systematic studies focusing on effects of particle/agglomerate size and distribution within ACL on PEMFC MEA reversal tolerance have not yet been reported. In order to be able to design anode catalyst layer structures that meet the performance and reversal tolerance requirements, it is necessary to obtain a better understanding of structure versus performance relationships. This requires the capability to fabricate different anode catalyst layer structures, as well as efforts in development of methodologies to characterize the spatial distribution of all components within the catalyst layers. The ultimate goal is to use the experimentally measured structural parameters as inputs for the development of models for understanding of the relationship between anode structures and reversal tolerance. The objective of this work is to present the methodology for structural characterization of anode catalyst layers containing OER materials. Structural parameters correlating with the extended reversal tolerance will be discussed. References S. Enz, T.A. Dao, M. Messerschmidt and J. Scholta, Investigation of degradation effects in polymer electrolyte fuel cells under automotive-related operating conditions, J. Power Sources, 274 (2015) 521-535 E. Antolini, Ir as catalyst and cocatalyst for oxygen evolution/reduction in acidic polymer electrolyte membrane electrolyzers and fuel cells, ACS Catal. 4 (2014) 1426-1440 T. Reier, M. Oezaslan and P. Strasser, Electrocatalytic oxygen evolution reaction (OER) on Ru, Ir and Pt catalysts: a comparative study of nanoparticles and bulk materials, ACS Catal. 2 (2012) 1765-1772 S. Cherevko, T. Reier, A.R. Zeradjanin, Z. Pavolek, P. Strasser and K.J.J. Mayrhofer, Stability of nanostructure iridium oxide electrocatalysts during oxygen evolution reaction in acidic environment, Electrochem. Commun. 48 (2014) 81-85 M. Bernicke, E. Ortel, T. Reier, A. Bergmann, J.F. de Araujo, P. Strasser and R. Kraerhnet, Iridium oxide coatings with template porosity as highly active oxygen evolution catalysts: structure-activity relationship, ChemSusChem, 8 (2015) 1908-1915
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".