Electronic Tunability of Ruthenium Formyl and Hydroxymethyl Intermediates Relevant to Sustainable CO-to-Methanol Conversion
Bibliographic record
Abstract
Metal formyl and hydroxymethyl complexes are implicated as key intermediates in the (photo)electrochemical reduction of carbon oxides (CO2 or CO) to liquid fuels such as methanol. Formyl complexes—and to a lesser extent hydroxymethyl complexes—have been previously synthesized and characterized; nevertheless, the influence of electronic modifications to ligands supporting these reactive carbon fragments is not well understood. Herein, we report the synthesis of a family of ruthenium polypyridyl carbonyl complexes of the form [Ru(4,4’-R,R-bpy)(tpy)(CO)] 2+ bearing different substituents on the bipyridyl (bpy) ligand (R = OMe, H, CF3). Treatment with NaBH4 as a chemical reductant results in formation of the formyl and subsequently the hydroxymethyl and methyl complexes; each are characterized by comprehensive NMR spectroscopy, mass spectrometry, and isotopic labeling studies. An electron-donating modification (R = OMe) to the bpy ligand is shown to significantly increase the lifetime of the formyl intermediate and the yield of released methanol. We observe a clear linear dependence of thermodynamic parameters on bpy electronics, however the stability of the formyl complex and the reactivity of the resulting hydroxymethyl complex do not depend linearly on ligand electronics. We anticipate that these results may be extended to future development of (photo)electrocatalytic systems for CO-to-methanol conversion.
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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".