Analysis of Stereochemistry Control in Homogeneous Olefin Polymerization Catalysis
Bibliographic record
Abstract
We propose a new method, using statistical analysis, to quantify factors contributing to stereo- and regiocontrol in olefin polymerization catalysis and competing β-hydrogen transfer to monomer. The method has been applied to three rather different Zr-based catalysts: ( 1 ) rac -Me 2 Si(3-Me-C 5 H 3 ) 2 ZrR +, a representative of “standard” ansa metallocenes; ( 2 ) [2,2′-bis(2-indenyl)biphenyl]ZrR +, a sterically congested and rather atypical metallocene; and ( 3 ) [1,2-(2- O -3- t Bu-C 6 H 3 CH 2 NMe) 2 -C 2 H 4 ]ZrR +, an example of an octahedral “ONNO”-type catalyst. The analysis produces separate numeric values for repulsive ligand–chain, chain–monomer, and ligand–monomer interactions. For insertion in the Zr– i Bu bond of 1 and 3, the ordering is syn (∼2.3 kcal/mol) > ligand–chain (∼1.6) > ligand–monomer (∼1.3), while for more crowded system 2 the three interactions are all around 2.0–2.5 kcal/mol. Despite the non-negligible magnitude of the ligand–chain interaction, the standard Corradini model of stereocontrol was found to apply for all cases. Our results also indicate that the stereocontrol penalties are sensitive to the nature of the chain and the olefin and that extrapolation from H or Me “chains” to more realistic chains is not warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".