Spectroelectrochemistry of water oxidation kinetics in molecular versus heterogeneous oxide iridium electrocatalysts
Bibliographic record
Abstract
Water oxidation is the step limiting the efficiency of electrocatalytic hydrogen production from water. Spectroelectro-chemical analyzes are employed to make a direct comparison of water oxidation reaction kinetics between a molecu-lar catalyst, the dimeric iridium catalyst [Ir2(pyalc)2(H2O)4-(µ-O)]2+ (IrMolecular, pyalc = 2-(2’pyridinyl)-2-propanolate) immobilized on a mesoporous indium tin oxide (ITO) substrate, with that of an heterogenous electrocatalyst, an amorphous hydrous iridium (IrOx) film. For both systems, four analogous redox states were detected, with the for-mation of Ir(4+)-Ir(5+) being the potential-determining step in both cases. However, the two systems exhibit distinct water oxidation reaction kinetics, with potential-independent first-order kinetics for IrMolecular contrasting with poten-tial-dependent kinetics for IrOx. This is attributed to water oxidation on the heterogenous catalyst requiring co-operative effects between neighboring oxidized Ir centers. The ability of IrMolecular to drive water oxidation without such co-operative effects is explained by the specific coordination environment around its Ir centers. These distinc-tions between molecular and heterogenous reaction kinetics are shown to explain the differences observed in their water oxidation electrocatalytic performance under different potential conditions.
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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 | 0.000 | 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".