The effect of phenolic compounds on salicylaldimine nickel-catalyzed ethylene oligomerization
Bibliographic record
Abstract
A salen nickel complex has been prepared in good yield with ethylenediamine, salicylaldehyde, and NiCl2·6H2O as raw materials. The structure of the complex was characterized by FTIR, 1H NMR, UV, and ESI-MS. Upon activation with methylaluminoxane (MAO), the precatalyst showed high activity for ethylene oligomerization. To reduce the simultaneous production of insoluble polymers during the nickel-catalyzed ethylene oligomerization, a series of phenolic compounds were introduced as modifiers for the production of linear α-olefins. The researched result showed that the phenolic compounds have a significant impact on the selectivity of oligomers and the concentration of polymers. With the increase of phenolic compounds, the content of polymers decreased and the distribution of oligomers gradually shifted toward lighter olefins. The sterically hindered 4-tert-butyphenol proved to be an especially efficient polymer-retarding modifier among the studied phenolic compounds in this work. Furthermore, the retarding effect of phenolic compounds toward insoluble polymers may be mainly related to their interaction with MAO, giving rise to larger MAO aggregates.
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) | 0.001 | 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".