Sequence-Defined Conjugated Oligomers in Donor–Acceptor Dyads
Bibliographic record
Abstract
Conjugated macromolecules have a rich history in chemistry, owing to their chemical arrangements that intertwine physical and electronic properties. The continuing study and application of these systems, however, necessitates the development of atomically precise models that bridge the gap between molecules, polymers, and/or their blends. One class of conjugated polymers that have facilitated the advancement of structure-property relationships is discrete, precision oligomers that have remained an outstanding synthetic challenge with only a handful of reported examples. Here we show the first synthesis of molecular dyads featuring sequence-defined oligothiophene donors covalently linked a to small-molecule acceptor. These dyads serve as a platform for probing complex photophysical interactions involving sequence-defined oligomers. This assessment is facilitated through the unprecedented control of oligothiophene length- and sequence-dependent arrangement relative to the acceptor unit, made possible by the incorporation of hydroxyl-containing side chains at precise positions along the backbone through sequence-defined oligomerizations. We show that both the oligothiophene sequence and length play complementary roles in determining the transfer efficiency of photoexcited states. Overall, the work highlights the importance of the spatial arrangement of donor-acceptor systems that are commonly studied for a range of uses, including light harvesting and photocatalysis.
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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) | 0.001 | 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".