Theoretical study of ethylene polymerization by phenoxy-imine catalyst
Bibliographic record
Abstract
Reaction mechanisms of ethylene polymerization catalyzed by the phenoxy-imine (FI) and the�nickel phenoxyphosphine polyethylene glycol (Ni-PEG) with alkali metals were explored using DFT calculations. For FI catalysts, the effect of group IVB transition metals substitutions was investigated. The trend of calculated activation energies (Ea) at the rate-determining step is Zr < Hf < Ti and is in good agreement with experiments. The effect of ligands of the Ti-FI-based catalysts when changing the parent nitrogen (O, N) to oxygen (O, O), phosphorus (O, P), and sulfur (O, S) ligands on activity was also monitored. The results indicated that the sulfur (O, S) ligand gives the lowest activation energy. Additionally, the reactivity of Ni-phenoxy-imine (Ni-FI)-based catalysts for polyethylene polymerization was studied. Our calculations suggested that the square planar complex of Ni-FI is more reactive than its C2 symmetric octahedral complex. For Ni-PEG(M) catalysts, the trend for activation energies of four Ni-PEG(M) systems is Li < Na < K < Cs, which corresponds to experimentally observed activities. Moreover, the roles of secondary metals in Ni-PEG catalysts in terms of steric, electronic, and electrostatic effects were elucidated. The DFT results suggested that the active catalyst should have strong cooperative metal-metal/metal-ligand interactions and less positive charge on the secondary metal. Finally, to gain insight into the design of the novel Ni-PEG catalysts with alkali-earth metals, the effect of catalyst structure on experimental activity was investigated. This work provides fundamental understandings of the reaction mechanisms for the FI and Ni-PEG(M) catalysts, which could be used for the design and development of catalysts for ethylene polymerization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".