Operando spectroelectrochemistry of redox state kinetics in water-oxidation IrOx electrocatalysts
Bibliographic record
Abstract
Hydrous iridium oxides (IrOx) are the best oxygen evolution electrocatalysts available for operation in acidic environments. In this study, we employ time-resolved operando spectroelectrochemistry to investigate the redox states kinetics of IrOx electrocatalyst films for both water and hydrogen peroxide oxidation. Three different redox species involving Ir3+, Ir4+ and Ir4.x are identified spectroscopically and their concentrations are quantified as a function of applied potential. The generation of Ir4.x+ states is found to be the potential determining step for catalytic water oxidation, whilst H2O2 oxidation is observed to be driven by the generation of Ir4+ states. The reaction kinetics for water oxidation, determined from the optical signal decays at open circuit, accelerate from ~ 20 s to <0.5 s with increasing applied potential above 1.3V vs. RHE (i.e. TOFs per active Ir state increasing from 0.05 to 2 s-1). In contrast, the reaction kinetics for H2O2 are found to be almost independent of the applied potential (increasing from 0.1-0.3 s-1 over a wider potential window), indicative of a first order reaction mechanism. These spectroelectrochemical data quantify the increase of both the density of active Ir4.x+ states and the TOFs of these states with applied positive potential, resulting in the observed sharp turn on of catalytic water oxidation current. We reconcile these data with the broader literature while providing a new kinetic insight into IrOx electrocatalytic reaction mechanisms, indicating a first order reaction mechanism for H2O2 oxidation driven by Ir4+ states, and a higher order reaction mechanism involving the co-operative interaction of multiple Ir4.x+ states for water oxidation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".