Operando tracking of oxidation-state changes by coupling electrochemistry with time-resolved X-ray absorption spectroscopy demonstrated for water oxidation by a cobalt-based catalyst film
Bibliographic record
Abstract
Transition metal oxides are promising electrocatalysts for water oxidation, i.e., the oxygen evolution reaction (OER), which is critical in electrochemical production of non-fossil fuels. The involvement of oxidation state changes of the metal in OER electrocatalysis is increasingly recognized in the literature. Tracing these oxidation states under operation conditions could provide relevant information for performance optimization and development of durable catalysts, but further methodical developments are needed. Here, we propose a strategy to use single-energy X-ray absorption spectroscopy for monitoring metal oxidation-state changes during OER operation with millisecond time resolution. The procedure to obtain time-resolved oxidation state values, using two calibration curves, is explained in detail. We demonstrate the significance of this approach as well as possible sources of data misinterpretation. We conclude that the combination of X-ray absorption spectroscopy with electrochemical techniques allows us to investigate the kinetics of redox transitions and to distinguish the catalytic current from the redox current. Tracking of the oxidation state changes of Co ions in electrodeposited oxide films during cyclic voltammetry in neutral pH electrolyte serves as a proof of principle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".