Triple-Detector GPC Characterization and Processing Behavior of Long-Chain-Branched Polyethylene Prepared by Solution Polymerization With Constrained Geometry Catalyst
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it