Multi-Scale Multi-Technique Characterization Approach for Analysis of PEM Electrolyzer Catalyst Layer Degradation
Bibliographic record
Abstract
Polymer electrolyte membrane water electrolyzers (PEMWEs) are devices of paramount importance, enabling the large-scale storage of hydrogen from intermittent renewable energy sources such as wind and solar. But a transition towards lower noble metal catalyst loadings and intermittent operation is needed for the widespread utilization of this technology. Although kinetic losses tend to dominate in membrane electrode assembly (MEA) results, it has been suggested that morphological changes and interfaces between the catalyst, ionomer, and membrane will also contribute to overall degradation. Moreover, the combination of degradation to the catalyst layer (CL) constituents will further lead to structural changes that have not been widely explored. The multitude and complexity of degradation mechanisms, which likely occur simultaneously, require a characterization approach that can explore surfaces and interfaces at a range of length-scales to probe chemical, morphological, and structural changes of constituents within the catalyst later. This paper presents a comprehensive characterization approach that features scanning electron microscopy (SEM), scanning transmission electron microscopy with energy-dispersive X-Ray spectroscopy (STEM/EDS), X-Ray photoelectron spectroscopy (XPS), X-Ray absorption spectroscopy (XAS), and transmission X-Ray microscopy (TXM) with X-Ray absorption near-edge structure (XANES) chemical mapping to study degradation of the catalyst layer with a focus on MEAs after intermittent and steady-state operation. Catalyst changes including dissolution, oxidation, and agglomeration were observed, as well as redistribution and dissociation of the ionomer. These smaller-scale changes were found to have a large influence on overall stability of the electrodes: they caused the formation of voids and segregation of constituents within regions of the film. Delamination and collapse of the overall catalyst layer were observed in some instances. Greater changes were observed after an extended 2 V hold compared to IV cycling, but similar degradation mechanisms were detected, which suggests the larger issues would likely also be experienced during intermittent PEMWE operation. These findings would not be possible without such a systematic, multi-scale, multi-technique characterization approach, which highlights the critical importance of detailed analysis of catalyst layer degradation to propose mitigation strategies and improve long-term PEM water electrolyzer performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".