DFT Studies on Substituent Effects in Palladium-Catalyzed Olefin Polymerization
Bibliographic record
Abstract
Gradient-corrected density functional theory has been used to study substituents effects on the cationic N ∧ N−Pd(II) diimine catalyst in ethene and propene polymerization. Here N ∧ N = −C(R)−N(Ar)−N(Ar)−C(R)− with R = H, −CH 3, −An and Ar = H, C 6 H 5, −2,6-C 6 H 3 (Me) 2, −2,6-C 6 H 3 ( i Pr) 2 . Calculations have been performed on the [N ∧ N-Pd(II)−P] + (P = n -propyl and isopropyl) alkyl complexes ( 1 ) and the corresponding [N ∧ N-Pd(II)−P(η 2 -CH 2 CHR o )] + π-complexes of ethene (R o = H) and propene (R o = CH 3 ), as well as the ethene and propene (1,2- and 2,1-) insertion transition states. The results show that an increase in the size of the substituents on the Pd(II) catalyst enhances the preference of 1 for the isomer with the branched isopropyl alkyl group P, while for the olefin complexes [N ∧ N-Pd(II)−P(η 2 -CH 2 CHR o )] + the isomer with the linear n -propyl group P becomes preferred. Further, an increase in the size of the substituents affects the relative binding of ethene and propene. Thus, the electronic preference of propene complexes is overridden by steric factors in the case of the largest substituents. The regioselectivity of propene insertion is strongly affected as well: an increase in steric demand decreases the 2,1-:1,2-insertion ratio, with 1,2-insertion becoming favored for the sterically most congested catalyst [R = −CH 3, Ar = −2,6-C 6 H 3 ( i Pr) 2 ].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".