Deconvoluting Reversible and Irreversible Degradation Phenomena in OER Catalyst Coated Membranes Using a Modified RDE Technique
Bibliographic record
Abstract
The suitability of the Thin-Film RDE (TF-RDE) technique to rigorously evaluate stability measurements for the oxygen evolution reaction (OER) was recently questioned. The main issue was the inability to deconvolute bubble blockage of catalytic active sites from catalyst dissolution using the TF-RDE technique. It is also possible that the low-loading of TF-RDE OER catalysts exacerbates the effect of bubble blockage. In this work, the modified rotating disk electrode (MRDE) is used with commercial catalyst coated membranes (CCMs) to evaluate catalyst stability. The MRDE may be better suited for stability measurements, since the CCM samples used can better avoid experimental artifacts and can explore much higher current densities than a TF-RDE. Thicker catalyst layers have good adhesion to the membrane, making experimental artifacts less pronounced in stability measurements. Three different stability protocols are used to study the effect of cycling, lower/upper potential limits, and regeneration. The protocol which induced the most irreversible degradation was the square-wave voltammetry (SWV) cycling between 0.05–2.0 VRHE. This irreversible degradation is likely the result of catalyst dissolution. The importance of differentiating between irreversible and reversible degradation is highlighted as a potential future standard for stability evaluation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".