Amino‐Modified Polymer Immobilized Ionic Liquid Stabilized Ruthenium Nanoparticles: Efficient and Selective Catalysts for the Partial and Complete Reduction of Quinolines
Bibliographic record
Abstract
Abstract RuNPs stabilised by amino‐decorated imidazolium‐based polymer immobilized ionic liquids catalyse the dimethylamine borane mediated reduction of quinolines to 1,2‐dihydroquinoline (DHQ) and 1,2,3,4‐tetrahydroquinoline (THQ). Partial reduction of 3‐substituted quinolines to the corresponding 1,2‐dihydroquinoline was achieved with 100 % selectivity in toluene under mild conditions. This is the first report of the selective partial reduction of 3‐substituted quinolines to the corresponding 1,2‐dihydroquinolines with a heterogeneous nanoparticle‐based catalyst. A wide range of substituted quinolines have also been reduced to the corresponding 1,2,3,4‐tetrahydroquinoline with high selectivity and good yields by adjusting the reaction time. The 1,2‐dihydroquinolines readily release dihydrogen in toluene at 60 °C in the absence of catalyst with no evidence for disproportionation and as such are potential organo‐hydride reagents. The initial TOF of 610 mol quinoline converted mol Ru −1 h −1 for the reduction of quinoline is among the highest to be reported for a metal nanoparticle‐based catalyst and the conversion of 96 % obtained after 4 h at 65 °C is significantly higher than its platinum nanoparticle counterpart PtNP@NH 2 ‐PEGPIILS as well as 5 wt/% Ru/C, which only reached 9 % and 11 % conversion, respectively, at the same time. Hot filtration experiments showed that the active species was heterogeneous.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".