A Density Functional Embedded Cluster Study of Proposed Active Sites in Heterogeneous Ziegler−Natta Catalysts
Bibliographic record
Abstract
A number of proposed models of the active sites in TiCl 4 /MgCl 2 heterogeneous Ziegler−Natta catalysts are examined using density functional methods. Using a number of different models for the surface, the sites formed when unreduced TiCl 4 is adsorbed onto MgCl 2 are predicted to be unstable. In contrast, TiCl 3 and TiCl 2 are found to bind strongly to MgCl 2 . Alternative models of the surface where one or all of the Mg atoms is replaced with Ti(II) give larger binding energies for TiCl 4 . Each step of the ethylene polymerization reaction is considered for each of the site models that are expected to be active. Two possible termination mechanisms, chain transfer to the monomer and β-hydrogen elimination, are also examined. The sites formed from TiCl 4 bind ethylene more weakly than those formed from TiCl 3 . No particular trends with respect to the type of site are found for the barrier to insertion, but the direction of approach of the ethylene molecule is important. The β-hydrogen elimination termination mechanism is predicted to be much less important than chain transfer to the monomer. From a comparison of ethylene insertion and termination barriers, the TiCl 3 -based sites on the TiCl 2 surface and the TiCl 3 -based Corradini site on MgCl 2 are shown to be very poor catalysts while the others, in particular the TiCl 3 -based edge site on MgCl 2, appear to be more promising models of the actual active sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".