A Solid-State <sup>13</sup>C NMR Investigation of the Morphology of Single-Site and Ziegler−Natta Linear Low-Density Polyethylenes with Varying Branch Contents
Bibliographic record
Abstract
The morphologies of 1-octene-based linear low-density polyethylenes (LLDPEs) prepared with single-site (ss) or Ziegler−Natta (ZN) catalysts were investigated using solid-state 13 C NMR spectroscopy. For each type of LLDPE, two samples, containing either approximately 10 or approximately 30 hexyl branches per 1000 backbone carbons, were studied. Mass fractions of their crystalline and amorphous phases as well as the interphase were quantified; a significant amount of LLDPE exists in the interphase for both types of samples, with their relative amounts decreasing with increasing branch content. Hexyl branches are approximately evenly distributed between the two noncrystalline phases for all samples except the high branch content ZN LLDPE, whose branches tend to cluster in the amorphous phase. The latter observation is attributed to the fact that most of the branched molecules in ZN LLDPE are in the low molar mass fraction and when the branch content is high these chains cannot fold into ordered structural units. The crystalline phase consists of three components with distinct 13 C spin−lattice relaxation, T 1, times; the degree of crystallinity decreases with increasing branch content. For samples with similar branch contents, ZN LLDPE tends to have thicker lamellae than does ss LLDPE.
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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) | 0.001 | 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".