Simultaneous Deconvolution of the Bivariate Distribution of Molecular Weight and Chemical Composition of Polyolefins Made with Ziegler‐Natta Catalysts
Bibliographic record
Abstract
Polyolefins made with Ziegler-Natta catalysts have non-uniform distributions of molecular weight (MWD) and chemical composition (CCD). The MWD is usually measured by high-temperature gel permeation chromatography (GPC) and the CCD by either temperature rising elution fractionation (TREF) or crystallization analysis fractionation (CRYSTAF). A mathematical model is needed to quantify the information provided by these analytical techniques and to relate it to the presence of multiple site types on Ziegler-Natta catalysts. We developed a robust computer algorithm to deconvolute the MWD and CCD of polyolefins simultaneously using Flory's most probable distribution and the cumulative CCD component of Stockmayer's distribution, which includes the soluble fraction commonly present in linear low-density polyethylene (LLDPE) resins and have applied this procedure for the first time to several industrial LLDPE resins. The deconvolution results are reproducible and consistent with theoretical expectations.
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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 | 0.000 |
| 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) | 0.000 | 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 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".