Stochastic Simulations of Polymer Growth and Isomerization in the Polymerization of Propylene Catalyzed by Pd-Based Diimine Catalysts
Bibliographic record
Abstract
A model is presented that employs a stochastic approach to the simulation of polyolefin chain growth and isomerization. The model is applied to propylene polymerization catalyzed by Pd-based diimine catalysts. The stochastic approach links the microscopic (quantum chemical) approach with modeling of the macroscopic systems. The DFT calculated energies of the elementary reactions and their barriers have been used as input parameters for the simulations. The influence of the catalyst's steric bulk, as well as polymerization temperature and olefin pressure on the polymer branching and its microstructure, is discussed. The results are in good agreement with available experimental data. In the propylene polymerization catalyzed by Pd(II) complexes with methyl backbone- and -Ph-(i)Pr(2) imine substituents a number of branches of 238 branches/1000 C have been obtained. An increase in polymerization temperature leads to a decrease in the number of branches. Change in olefin pressure does not affect the global number of branches, while it strongly affects the polymer microstructure, leading to hyperbranched structures at low pressures. Further, the simulations confirm the experimental interpretation of the mechanistic details for this process: (1) both 1,2- and 2,1-insertion happen with the ratio of ca. 7:3; (2) there are no insertions at the secondary carbons; and (3) most of the 2,1-insertions are followed by a chain straightening isomerization. Thus, for this catalyst the total number of branches is controlled exclusively by the 1,2-/2,1-insertion ratio. For the catalysts with different substituents the branching can be controlled by a 1,2-/2,1-insertion ratio as well as the fraction of the insertions at the secondary carbons. The results of the present studies demonstrate that a stochastic approach can be successfully used to model the polyolefin microstructures and their catalyst, temperature, and pressure dependence. Further, it can also facilitate interpretation of the experimental results, and can be used to draw general conclusions about the influence of the specific elementary reaction barriers on the polymer structures; this can be helpful for a rational design of the catalysts producing a desired microstructure.
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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 | 0.001 |
| 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) | 0.000 | 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".