Pinwheel-like Curved Aromatics from the Cyclotrimerization of Strained Alkyne Cycloparaphenylenes
Bibliographic record
Abstract
Carbon nanomaterials composed of curved aromatics, such as carbon nanotubes, are difficult to selectively synthesize and modify precisely. Smaller molecular fragments of curved nanomaterials, such as cycloparaphenylenes, benefit from the precision of bottom-up synthesis, however, efforts to expand the curved molecular framework into even larger structures often rely on restrictive early stage synthetic strategies or difficult to control polymerizations. In this work we report a high yielding, strain-promoted, late-stage modification of a series of [ n + 1]CPPs. We show that the conversion of these [ n + 1]CPPs into soluble, pinwheel-like multipore carbon nanostructures is achievable via a straightforward and efficient metal-mediated alkyne cyclotrimerization reaction. We provide insight into suitable metals for this transformation, the photophysics of these trimeric molecules, as well as their strain profiles and crystal packing. We also demonstrate the strain-enhanced nature of the reaction under the optimized conditions, showing that strained internal-alkyne [ n + 1]CPPs efficiently undergo complete conversion whereas unstrained diphenylacetylene remains completely unreacted. We anticipate that this work will have broader impacts on the study of reactivity in strained-alkyne-containing hydrocarbons, and that access to this new molecular architecture will inspire new research and applications in materials science and related fields.
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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".