Synthesis of Hyperbranched Poly(phenylacetylene)s Containing Pendant Alkyne Groups by One-Pot Pd-Catalyzed Copolymerization of Phenylacetylene with Diynes
Bibliographic record
Abstract
We report in this article the convenient synthesis of a class of hyperbranched poly(phenylacetylene)s (HBPPAs) containing various branching densities and different contents of pendant alkyne groups through one-pot chain-growth copolymerization of phenylacetylene (PA) with a diyne comonomer (1,3- or 1,4-diethynylbenzene (DEB)). The polymerization was facilitated with the use of an in situ generated cationic diphosphine-ligated Pd(II) catalyst system. Serving effectively as difunctional cross-linker in the polymerization, the diyne comonomer first undergoes monoinsertion to render pendant alkyne groups, which can be further enchained to generate branching structures. A systematic investigation has been undertaken to study the effects of various polymerization parameters, including diyne/PA feed ratio, diyne type, temperature, and solvent, on the polymerization, polymer structure and topology. With either 1,3- or 1,4-DEB, a convenient tuning of polymer topology from linear to hyperbranched can be achieved by simply increasing diyne/PA feed ratio. Relative to 1,3-DEB, 1,4-DEB is more effective in rendering branching structures since the pendant alkyne groups suffer less steric effect from the polymer backbone and are thus more reactive. As to the solvent, a dichloromethane/methanol mixture (at vol. ratio of 13:3) was shown to better help the formation of branching structures than methanol alone as the resulting polymers can dissolve well in the former while precipitate in the latter. Because of their possession of the valuable pendant alkyne groups, these polymers have also been demonstrated for their use as building blocks in the synthesis of core–shell structured star polymers containing a HBPPA core and polystyrene arms through their Cu-catalyzed “click” reaction with an azide-ended polystyrene.
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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".