Tuning Bis(imino)pyridyl Iron‐Catalyzed Ethylene Oligomerization by Modification of MAO with <i>p</i>‐BrPhOH
Bibliographic record
Abstract
Abstract To reduce the simultaneous production of insoluble polymers during the bis(imino)pyridyl iron‐catalyzed ethylene oligomerization, in this study, p‐BrPhOH (4‐bromophenol) has been chosen as the most optimal modifier for the production of linear α‐olefins. It is found that the polymer share in the total products is largely reduced with the use of p‐BrPhOH as the modifier. The catalytic system also possesses a high activity with the liquid production maintained high level of linearity. Moreover, the introduction of p‐BrPhOH promoted the high‐temperature stability of the catalytic system, leading to the enhanced oligomerization activity as the catalytic system can catalyze ethylene oligomerization at higher temperatures. A characterization of the catalytic system with electron paramagnetic resonance shows that introduction of p‐BrPhOH significantly inhibits the formation of ferric ions, which can be the main active centers responsible for generating undesired insoluble polymers, thus this can largely retard the production of insoluble polymers during ethylene oligomerization.
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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) | 0.000 | 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 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".