Exploring the Scope of Possible Microstructures Accessible from Polymerization of Ethylene by Late Transition Metal Single-Site Catalysts. A Theoretical Study
Bibliographic record
Abstract
The stochastic simulations of the polymer growth under different reaction conditions (temperature and pressure) have been performed for the ethylene polymerization processes catalyzed by late transition metal complexes. The processes catalyzed by the real Pd-based diimine catalyst have been studied on the basis of the energetics of elementary reactions (DFT-calculated and experimental). The simulations with systematically changed insertion barriers have also been performed to model the influence of different catalysts, going beyond diimine systems. In the case of diimine catalysts, the simulations reproduce the experimentally observed average number of branches as well as the temperature and pressure dependencies of the branching numbers. The results explain the microscopic origin of an opposite temperature effect observed for ethylene and propylene polymerization. Further, the simulations allow one to understand the different pressure effect observed for Pd- and Ni-based diimine catalysts. The model simulations give insight into the role of particular factors controlling the polyethylene branching. It has been found that an energy difference between the activation barriers for the primary and secondary insertions strongly influences the polymer microstructure. The results demonstrate that a wide range of polymer topologies can be potentially obtained from ethylene polymerizations with various single-site catalyst characterized by different energetics of the catalytic cycle. Thus, it seems to be possible to rationally design a catalyst producing a desired polyethylene microstructure.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".