Transient Surface Degradation of LSCO and LSFO during OER in Alkaline Electrolyte under Dynamic Cycling Conditions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A fundamental understanding of the relationship between activity and stability is essential for rationalizing materials used as catalysts for the oxygen evolution reaction (OER). In the case of oxide perovskites, the catalytic activity of OER catalysts is often linked to the electronic structure, such as the degree of hybridization and occupation of the O 2p and transition metal 3d orbitals (TM 3d–O 2p), but their transient behavior during catalyst lifetime and degradation remains poorly understood. To address this, an epitaxial model system approach is utilized, comparing La 0.6 Sr 0.4 CoO 3−δ (LSCO) and La 0.6 Sr 0.4 FeO 3−δ (LSFO) as model OER catalysts. Both materials show distinctly different degradation mechanisms under dynamic cycling, namely a surface-passivation-mediated degradation in LSFO and an extended bulk degradation in LSCO. This contrasting behavior is consistently observed in their postcatalysis morphology, crystallinity, electronic structure, and electrochemical redox behavior. Our findings indicate a correlation between the hosted transition metal and the dominant degradation mechanism. This relationship leads to distinct transient behaviors of electronic structure, morphology, and OER activity. It also indicates a systematic loss of available hybrid electronic states over the catalyst’s lifetime.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".