Horizons in catalyst-transfer polymerization research
Bibliographic record
Abstract
This review provides a comprehensive analysis of catalyst-transfer polymerization (CTP) methods, encompassing established approaches such as Kumada, Suzuki-Miyaura, Stille, Negishi, Murahashi and Sonogashira, alongside emerging metal-free techniques. It explores the unique features, strengths, and limitations of each method, focusing on both their mechanistic and structural aspects. A critical evaluation of catalyst performance across these methods highlights their comparative effectiveness and the impact of various catalysts on polymer properties, emphasizing the importance of catalyst and ligand selection in achieving precise polymer architectures. This review also discusses recent advancements in catalyst engineering, including novel ligands and multi-metal systems, offering valuable insights into optimizing CTP processes. By addressing challenges and opportunities in the field, this review aims to guide researchers in advancing CTP methodologies, fostering innovation in organic electronics and high-performance polymer applications. • Reveals advancements in catalyst-transfer polymerization methods for precision materials. • Explores ligand engineering's role in controlled polymer architectures. • Discusses new catalysts for broadening the scope of conjugated polymer synthesis. • Reviews mechanistic insights into Kumada and Suzuki-Miyaura polymerization techniques. • Offers guidance for future research in organic electronics and high-performance polymers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".