Synthesis of a Well-Defined Conjugated Polymer Via Nickel-Catalyzed Direct Arylation Polymerization (Ni-DArP)
Bibliographic record
Abstract
Abstract Direct arylation polymerization (DArP) is a sustainable method of conjugated polymer synthesis; however, expensive and low-abundance palladium catalysts are nearly ubiquitous in this chemistry. Catalysts based on earth-abundant first-row transition metals are little-explored but would represent a significant improvement in the sustainability of DArP and potentially enable new reactivity. Beyond previously explored copper catalysts, nickel catalysts offer access to possibly more effective polymerization chemistry. Previous reports of DArP using a Ni catalyst yielded cross-linked, insoluble materials. Here we report the first instance of a linear, well-defined conjugated polymer synthesized using Ni-catalyzed DArP (Ni-DArP) where 5,5′-(2,5-bis(hexyloxy)-1,4-phenylene)dithiazole was copolymerized with 1,4-dibromo-2,5-bis((2-ethylhexyl)oxy)benzene using Ni(OAc)2. Polymerizations proceeded at catalyst loadings of 5–20 mol % affording polymers with molar masses up to 10.2 kg/mol and yields up to 55%, on-par with Cu-DArP. The stable, commercial species Ni(OAc)2·4H2O can be easily dehydrated to yield the active catalyst. Regioselectivity for the 2-position of thiazole with both Ni and Cu was observed, contrasting selectivity for the 5-position with Pd, demonstrated in both polymerizations and small-molecule couplings, along with the Ni-catalyzed conditions potentially favoring ring walking relative to that of Cu. The complementary nature of the catalysts demonstrates the value of developing earth-abundant catalysts for DArP beyond the relevance to sustainability.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".