What Limits the Molecular Weight and Controlled Synthesis of Poly(3-alkyltellurophene)s?
Bibliographic record
Abstract
Abstract Polytellurophenes are an emerging class of conjugated polymers; however, their controlled polymerization leading to high molecular weight materials has been a major challenge. Here we present a systematic investigation of the synthesis of poly(3-alkyltellurophene)s using the catalyst transfer polycondensation methodology. Learning that previous syntheses were limited by both polymerization reaction kinetics and polymer solubility, we design new tellurophene monomers to overcome these limitations. Controlled polymerization behavior up to Mn = 25 kDa, chain extension, block copolymerization, external initiation, and well-defined end groups are demonstrated for poly(3-alkyltellurophene)s with appropriately designed side chains. We clarify the role that side-chain branching point plays on polymerization kinetics and optical properties for these prototypical regioregular polymers. In addition, the effect that monomer addition sequence has on well-defined tellurophene–thiophene block copolymers was studied. The controlled polymerization of tellurophene should provide access to more complex polymeric architectures involving these and other conjugated monomers. The methods used to optimize the polymerization of alkyltellurophenes should be applicable to other monomers that have been challenging to synthesize in a controlled manner.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".