Direct (Hetero)arylation Polymerization: Trends and Perspectives
Bibliographic record
Abstract
Conjugated polymers have attracted much attention in recent years, as they can combine the best features of metals or inorganic semiconducting materials (excellent electrical and optical properties) with those of synthetic polymers (mechanical flexibility, simple processing, and low-cost production), thereby creating altogether new scientific synergies and technological opportunities. In the search for more efficient synthetic methods for the preparation of conjugated polymers, this Perspective reports advances in the field of direct (hetero)arylation polymerization. This recently developed polymerization method encompasses the formation of carbon-carbon bonds between simple (hetero)arenes and (hetero)aryl halides, reducing both the number of synthetic steps and the production of organometallic byproducts. Along these lines, we describe the most general and adaptable reaction conditions for the preparation of high-molecular-weight, defect-free conjugated polymers. We also discuss the bottleneck presented by the utilization of certain brominated thiophene units and propose some potential solutions. It is, however, firmly believed that this polymerization method will become a versatile tool in the field of conjugated polymers by providing a desirable atom-economical alternative to standard cross-coupling polymerization reactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".