Modelling and parameter estimation of <i>trans</i> ‐ <i>β</i> ‐farnesene coordination polymerization
Bibliographic record
Abstract
Abstract trans ‐ β ‐Farnesene is a bio‐derived terpene monomer that can polymerize, generating polymers with properties that can be similar to the properties of conventional petroleum‐derived polymers. For this reason, in the present study, several coordination polymerizations of trans ‐ β ‐farnesene are carried out using the Ziegler–Natta catalyst system composed by neodymium versatate (), diisobutylaluminum hidride (DIBAH), and dimethyldichlorosilane (DMDCS) in order to evaluate the influence of key operation variables on the control of average molar masses and monomer conversion. A phenomenological model is proposed to describe the coordination polymerization of trans ‐ β ‐farnesene, and the kinetic parameters required to simulate the reactions are estimated. The initial concentration of DIBAH used as a chain transfer agent (CTA) is calculated by a data reconciliation procedure since this very active compound can participate in undesired side reactions. It is shown that the initial monomer, DIBAH, and concentrations exert strong influences on the monomer conversion and average molar masses of (poly)farnese while the temperature effect is not so pronounced. The proposed kinetic mechanism was able to predict well the experimental data collected during the reactions, with the successful reconciliation of CTA concentrations and estimation of model parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".