Substrate‐Mediated Deactivation of a Ru(P<i><sup>t</sup></i><sup>Bu</sup><sub>2</sub>N<sup>Bn</sup><sub>2</sub>) Cooperative Complex
Bibliographic record
Abstract
Abstract Ligand design for metal–ligand cooperative (MLC) catalysis is inherently more complex than that for traditional non‐cooperative ligands. The basicity, sterics and structure of the acid/base group in MLC proton‐transfer (PT) complexes, for instance, undoubtedly influence catalyst performance. Herein, we evaluate the highly tunable PR2NR′2 (1,5‐R′‐3,7‐R‐1,5‐diaza‐3,7‐diphosphacyclooctane) ligand family for the first time in an organic transformation. With [Ru(Cp)(PtBu2NBn2)(MeCN)][PF6] as the catalyst, no turnover was observed in the anti‐Markovnikov hydration of alkynes, a known PT MLC reaction. Treatment of the cooperative complex with phenylacetylene affords a vinylammonium product in which the pendant nitrogen atom of the PtBu2NBn2 ligand forms a Lewis acid–base adduct with the alpha‐carbon atom of the vinylidene intermediate. Characterization by X‐ray crystallography and NMR spectroscopy conclusively assign this structure in both the solid and the solution state. The adduct formation is irreversible, and the adduct is characterized as a catalyst deactivation product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".