Insight into Water Oxidation by Mononuclear Polypyridyl Ru Catalysts
Bibliographic record
Abstract
A family of compounds based on the mononuclear coordination complex [Ru(tpy)(bpy)(OH(2))](2+) (1b; tpy = 2,2':6',2''-terpyridine, bpy = 2,2'-bipyridine) are shown to be competent catalysts in the Ce(IV)-driven oxidation of water in acidic media. The systematic installation of electron-withdrawing (e.g., -Cl, -COOH) and -donating (e.g., -OMe) groups at various positions about the periphery of the polypyridyl framework offers insight into how electronic parameters affect the properties of water oxidation catalysts. It is observed, in general, that electron-withdrawing groups (EWGs) on the bpy ligands suppress catalytic activity (k(obs)) and enhance catalytic turnover numbers (TONs); conversely, the presence of electron-donating groups (EDGs) accelerate catalytic rates while decreasing catalyst stability. We found that 2,2'-bipyridine N,N'-dioxide is produced when 1b is subject to excess Ce(IV) in acidic media, which suggests that dissociation of the bpy ligand is a source of catalyst deactivation and/or decomposition. Density functional theory (DFT) calculations corroborate these findings by showing that the Ru-N(bpy) bond trans to the O atom is weakened at higher oxidation levels while the other Ru-N bonds are affected to a lesser extent. We also show that the Ru-Cl bond is not robust in aqueous media, which has implications in studying the catalytic behavior of systems of this type.
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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 | 0.000 |
| 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".