Triple-Detector GPC Characterization and Processing Behavior of Long-Chain-Branched Polyethylene Prepared by Solution Polymerization With Constrained Geometry Catalyst
Bibliographic record
Abstract
Abstract Fourteen long-chain branched (LCB) polyethylene (PE) samples were prepared by a constrained geometry catalyst. The PE samples had average branching frequencies of 0.06–0.98 branches per polymer chain, as determined by the nuclear magnetic resonance spectroscopy (13C NMR). These samples, as well as five linear PEs were characterized using a gel permeation chromatography (GPC) coupled with online three-angle laser light scattering (LS), differential refractive index (DRI), and viscosity (CV) detectors. The root mean-square radius of gyration ( 〈 r g 2 〉 1 / 2 ) , intrinsic viscosity ([η]), and molecular mass (M) of the PEs were measured for each elution fraction. Based on the comparison of the long-chain branching (LCB) PEs with their linear counterparts and the Zimm–Stockmayer equation, the distributions of long-chain branch frequency (LCBF) and density (LCBD) as function of molecular mass were estimated. It was found that although the LCBF increased with the increase of molecular mass, the LCBD showed a maximum value in the medium molecular mass range for most of the PE samples. The average LCBD data from the GPC analysis were in good agreement with the 13C NMR measurements. The rheological properties and processing behavior of these samples were also assessed. While the long chain branching showed significant effects on the modulus and viscosity, it did not improve the processing. Compared to linear PE, polymer melt flow instabilities such as sharkskin, stick-slip and gross melt fracture developed in extrusion of LCB PEs occurred at lower wall shear stresses and apparent shear rates.
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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 | 0.001 |
| 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".