Examining the Spin State and Redox Chemistry of Ni(Diimine) Catalysts during the Synthesis of π‐Conjugated Polymers
Bibliographic record
Abstract
Abstract Kumada catalyst transfer polymerization (KCTP) is currently an unparalleled catalytic method for preparing π‐conjugated materials with well‐defined end‐groups, targeted molecular weights, and narrow dispersity. Polymerization control is both catalyst and monomer dependent. Polymerizations using bidentate Ni(phosphine) catalysts and 3‐alkylthiophene (or related) monomers lead to the highest degrees of polymerization control; however the scope of monomers that Ni(phosphines) can polymerize is quite limited. Here, two possible mechanistic limitations of Ni(diimine) catalysts are evaluated: 1) the role of spin states, and 2) the redox non‐innocence of diimine ligands. Density functional theory (DFT) calculations are utilized to evaluate singlet and triplet spin states of Ni(diimine) species throughout the KCTP catalytic cycle. It is found that in the energetically favored triplet state, Ni(diimine) catalyst dissociation from polymer chains is competitive with intramolecular oxidative addition, which reduces polymerization control. The redox activity of Ni(diimine) catalysts during polymerization is investigated and it is determined that a Ni(diimine)X2 precatalyst is converted into Ni(diimine)2 under reductive conditions analogous to KCTP, but surprisingly this species is also active during polymerization which leads to an interesting off‐cycle pathway that has not been previously considered. These insights differentiate the mechanisms of Ni(diimine) and Ni(phosphine) polymerizations, highlighting catalyst spin and redox activity as additional factors in KCTP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".