Simultaneous Deconvolution of the Molecular Weight and Chemical Composition Distribution of Polyolefins Made with Ziegler‐Natta Catalysts
Bibliographic record
Abstract
Abstract Heterogeneous Ziegler‐Natta catalysts produce polyolefins that have broad distributions of molecular weight (MWD) and chemical composition (CCD). For such broad distributions, mathematical models are useful to quantify the information provided by polyolefin analytical techniques such as high‐temperature gel permeation chromatography (GPC), temperature rising elution fractionation (TREF), and crystallization analysis fractionation (CRYSTAF). In this paper, we developed a mathematical model to deconvolute the MWD and CCD of polyolefins simultaneously, using Flory's most probable distribution and the cumulative CCD component of Stockmayer's distribution. We have applied this procedure to “model” polyolefin resins and to one industrial linear low‐density polyethylene (LLDPE) resin. The proposed methodology is able to deconvolute theoretical distributions even when random noise is added to the MWDs and CCDs, and it can be used to calculate the minimum number of active site types on heterogeneous Ziegler‐Natta catalysts.
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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".