Modeling of sequence length distribution for olefin copolymerization with vanadium‐based catalyst
Bibliographic record
Abstract
Abstract Some thermodynamic and mechanical properties of a polyolefin, such as the melting temperature and the rigidity, are dependent on the nature of its sequence distribution. Accurate modeling of sequence length distribution (SLD) is important in precisely tuning and optimizing the properties of polymers produced. In this article, we proposed a model to predict the molecular weight distribution (MWD) and SLD for olefin copolymerization with vanadium‐based catalyst. Due to the 2,1‐insertion of α‐olefin with vanadium‐based catalyst, the SLD is expressed by uninterrupted methylene sequence distribution instead of conventional triad sequence distribution. To obtain a reliable model, parameter estimation with experimental data is first conducted. The SLD model along with the estimated kinetic parameters can be used to predict unmeasurable sequence length fraction. For the experimental conditions studied, the average methylene sequence length is predicted to change from 10 to 4 units as the propylene/ethylene mole feed ratio increases from 1.1 to 3.4.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".