Controlling π-stacking interactions in a series of novel heteroacene derivatives
Bibliographic record
Abstract
The understanding and control of intermolecular forces allows for the creation of supramolecular architectures held together by relatively weak, flexible interactions.The exploitation of π-π stacking interactions can produce materials with dynamic properties such as crystal to crystal transitions.[1]Co-facial π-interactions are also important in the preparation of semiconducting organic materials,[2] however, face-to-face π-stacking is generally repulsive and often disfavoured.[3] In our development of an SNAr-based methodology for the synthesis of heteropentacene analogues 1a-c we synthesised a series of electronically biased 1,2,3,4-tetrasubstituted dibenzodioxin (2a-c) and phenoxazine (3a-c) derivatives.[4]An examination of the crystal structures of 2a-c and 3a-c indicates that a combination of electronic bias and C-H substitution affords compounds which tend to π-stack in a co-facial, antiparallel manner.A search of the Cambridge Structural database for representative structures was also conducted.The results indicate such motifs could be valuable building blocks for supramolecular design of materials held together by co-facial π-π stacking interactions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".