Study on single and binary catalytic systems of pyridine‐imine catalysts based on nickel and iron in synthesis of reactor blends and low‐density polyethylene nanocomposites
Bibliographic record
Abstract
ABSTRACT In the presence of modified methylaluminoxane as cocatalyst, the behavior of a binary catalytic system based on pyridine‐imine nickel (N) and iron (F) catalysts was evaluated in order to reach a proper mixture of polyethylene (PE). A computational study along with kinetic profile suggested that the catalyst F with higher electron affinity (A) and electrophilicity (ω) in the methyl cationic active center and stronger interaction with the monomer led to high integrated monomer consumption and higher activity. In addition, the samples produced by the mixture of catalysts showed a higher value of [19.4 × 104 g (PE) mol (Fe+Ni)−1 h−1)], melting point (127.8 °C), and crystallinity extent (41.29%) than the samples produced by the single catalysts. The addition of multiwalled carbon nanotubes (MWCNT) into the polymerization media reduced the activity of catalysts [from 7.50 × 104 to 0.66 × 104 g (PE) mol (Fe+Ni)−1 h−1] and the thermal properties of the low‐density polyethylene nanocomposite samples. However, the sample containing 2.33% MWCNT20‐30 improved the total thermal stability of the neat polyethylene blend up to 400 °C. Scanning electron microscope images of the samples demonstrated irregular to virtually uniform morphologies were obtained through the in situ and solution‐mixing techniques. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47376.
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".