Olefin Metathesis in Water: Speciation of a Leading Water-Soluble Catalyst Pinpoints Challenges and Opportunities for Chemical Biology
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The metathetical modification of biomolecules in aqueous environments holds great promise for advances at the interface of chemistry, biology, and medicine. However, rapid degradation of the metathesis catalysts necessitates their use in large stoichiometric excess, resulting in undesired side-reactions promoted by the ruthenium products. Although water is now known to play a central role in catalyst decomposition, the elusive nature of the intermediates has hampered insight into the pathways involved. We describe the detailed speciation in water of AquaMet ( AM ), the dominant ruthenium catalyst used for aqueous metathesis, and implications for catalysis. Potentiometric and spectroscopic speciation studies reveal that only trace AM is present under the pH-neutral, salt-free conditions routinely employed in synthetic applications of aqueous metathesis. Instead, metathesis-inactive hydroxide species dominate. Even at pH 3, Ru–H 2 O complexes dominate in 0.01 M NaCl (aq), and the water ligands are readily deprotonated as the pH is increased. Raising NaCl (aq) concentrations to 1 M suppresses deprotonation events below pH 8, stabilizing AM as the dominant solution species at neutral pH, and significantly expanding the metathesis-compatible regime. Hitherto unrecognized catalyst solubility issues are also revealed, pointing toward avenues for advance. More broadly, the capacity to directly link catalyst environment to structure and performance opens new opportunities for olefin metathesis in complex, water-rich settings.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".