Polypropylene Made with In‐Situ Supported Me<sub>2</sub>Si(Ind)<sub>2</sub>ZrCl<sub>2</sub> and Me<sub>2</sub>Si(2‐Me‐Ind)<sub>2</sub>ZrCl<sub>2</sub> Catalysts: Properties Comparison
Bibliographic record
Abstract
Abstract Summary: Propylene homopolymerizations were carried out using Me2Si(Ind)2ZrCl2 and Me2Si(2‐Me‐Ind)2ZrCl2, MAO‐modified silica, and common alkylaluminum cocatalysts. Supported catalysts were prepared by the in‐situ immobilization technique. The effect of the type and concentration of alkylaluminum on propylene polymerization was evaluated using TEA (triethylaluminum), IPRA (isoprenylaluminum), and TIBA (triisobutylaluminum) as cocatalysts. The polymers were analyzed by gel permeation chromatography (GPC), differential scanning calorimetry (DSC), and scanning electronic microscopy (SEM). The effect of the type and concentration of alkylaluminum on the melting temperature and the molar mass of the polypropylene was the same for both catalysts. The polymers made with in‐situ supported catalyst had lower melting points and, in almost all polymerization conditions, higher molar masses than those produced by homogeneous polymerization. Polypropylene samples made with Me2Si(2‐Me‐Ind)2ZrCl2 had higher melting temperatures and molar masses than those made with Me2Si(Ind)2ZrCl2. SEM micrographs showed that the polymers obtained with in‐situ supported systems had a well‐defined morphology, confirming that the polymerization indeed took place onto the silica support. SEM micrographs of polypropylene particles obtained with Me2Si(2‐Me‐Ind)2ZrCl2 in the presence of IPRA. magnified image SEM micrographs of polypropylene particles obtained with Me2Si(2‐Me‐Ind)2ZrCl2 in the presence of IPRA.
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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) | 0.001 | 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".