Quantitative determination of short‐chain branching content and distribution in commercial polyethylenes by thermally fractionated differential scanning calorimetry
Bibliographic record
Abstract
Abstract A method for rapid quantitative analysis of the content and distribution of short chain branching (SCB) for α‐olefin/ethylene copolymers based on thermally fractionated DSC is presented. Eight commercial polyethylenes, four made with conventional Ziegler‐Natta catalysts and four made with metallocene catalysts, were analyzed by differential scanning calorimetry (DSC), after having been thermally segregated by successive nucleation annealing (SNA). The polyethylenes were also analyzed by temperature rising elution fractionation (TREF) and carbon‐13 nuclear magnetic resonance (13C‐NMR). The SNA‐DSC procedure segregates polyethylenes according to methylene sequence lengths (MSL). The relationship between DSC melting temperature and SCB content was obtained by calibration with linear hydrocarbons; TREF results were not used in the SNA‐DSC calibration. Deconvolution of the SNA‐DSC endotherms yielded estimates of the average SCB contents and SCB distributions. The SCB contents obtained from the SNA‐DSC for linear low density polyethylenes agreed very well with the SCB contents obtained by 13C‐NMR and TREF, and the SCB distributions measured by SNA‐DSC were very similar to those obtained by TREF. The SCB contents obtained by SNA‐DSC for ultra‐low density polyethylenes, made with metallocene catalysts, were about 20% lower than the values obtained by 13C‐NMR; the values obtained by TREF were even lower.
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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 | 0.001 | 0.001 |
| 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".