Systematic Influence of Electronic Modification of Ligands on the Catalytic Rate of Water Oxidation by a Single‐Site Ru‐Based Catalyst
Bibliographic record
Abstract
Abstract Catalytic water oxidation is an important process for the development of clean energy solutions and energy storage. Despite the significant number of reports on active catalysts, systematic control of the catalytic activity remains elusive. In this study, descriptors are explored that can be correlated with catalytic activity. [Ru(tpy)(pic)2(H2O)](NO3)2 and [Ru(EtO‐tpy)(pic)2(H2O)](NO3)2 (where tpy=2,2′ : 6′,2“‐terpyridine, EtO‐tpy=4′‐(ethoxy)‐2,2′:6′,2”‐terpyridine, pic=4‐picoline) are synthesized and characterized by NMR, UV/Vis, EPR, resonance Raman, and X‐ray absorption spectroscopy, and electrochemical analysis. Addition of the ethoxy group increases the catalytic activity in chemically driven and photocatalytic water oxidation. Thus, the effect of the electron‐donating group known for the [Ru(tpy)(bpy)(H2O)]2+ family is transferable to architectures with a tpy ligand trans to the Ru‐oxo unit. Under catalytic conditions, [Ru(EtO‐tpy)(pic)2(H2O)](NO3)2 displays new spectroscopic signals tentatively assigned to a peroxo intermediate. Reaction pathways were analyzed by using DFT calculations. [Ru(EtO‐tpy)(pic)2(H2O)](NO3)2 is found to be one of the most active catalysts functioning by a water nucleophilic attack mechanism.
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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 | 0.000 | 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".