Atomically Dispersed Iridium on Indium Tin Oxide Efficiently Catalyzes Water Oxidation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Heterogeneous catalysts in the form of atomically dispersed metals on a support provide the most efficient utilization of the active component, which is especially important for scarce and expensive late transition metals. These catalysts also enable unique opportunities to understand reaction pathways through detailed spectroscopic and computational studies. Here, we demonstrate that atomically dispersed iridium sites on indium tin oxide prepared via surface organometallic chemistry display exemplary catalytic activity in one of the most challenging electrochemical processes, the oxygen evolution reaction (OER). In situ X-ray absorption studies revealed the formation of Ir V ═O intermediate under OER conditions with an Ir–O distance of 1.83 Å. Modeling of the reaction mechanism indicates that Ir V ═O is likely a catalyst resting state, which is subsequently oxidized to Ir VI enabling fast water nucleophilic attack and oxygen evolution. We anticipate that the applied strategy can be instrumental in preparing and studying a broad range of atomically dispersed transition metal catalysts on conductive oxides for (photo)electrochemical applications.
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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 | 0.000 |
| 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".