A systematic computational study of electronic effects on hydrogen sensitivity of olefin polymerization catalysts (abstract only)
Bibliographic record
Abstract
One of the important product parameters of polyolefins is their molecular weight (distribution). A common way to control this parameter is to add molecular hydrogen during the polymerization, which then acts as a chain transfer agent. The factors governing the hydrogen sensitivity of olefin polymerization catalysts are poorly understood and have attracted little attention from computational chemists. To explore the electronic factors determining hydrogen sensitivity we performed density functional calculations on a wide range of simple model systems including some metallocenes and a few basic models of heterogeneous catalysts. As a quantitative measure for hydrogen sensitivity we used the ratio of (i) the rate constant for chain transfer to hydrogen to (ii) the rate constant for ethene insertion, k(h)/k(p) (see the scheme below), and as a measure of electrophilicity we used the energy of complexation to the probe molecule ammonia. [Formula: see text] For isolated species in the gas phase, complexation energies appear to dominate the chemistry. Ethene complexes more strongly than hydrogen and with increasing electrophilicity of the metal centre this difference grows; the hydrogen sensitivity decreases accordingly. Although many factors (like catalyst dormancy and deactivation issues) complicate the comparison with experiment, this result seems to agree both in broad terms with the experimental lower hydrogen sensitivity of heterogeneous catalysts, and more specifically with the increased hydrogen sensitivity of highly alkylated or fused metallocenes. The opposite conclusion reached by Blom (see Blom et al 2002 Macromol. Chem. Phys. 203 381-7) is due to the use of a very different measure of electrophilicity, rather than to different experimental data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".