Relation between the Electronic Properties of Regioregular Donor–Acceptor Terpolymers and Their Binary Copolymers
Bibliographic record
Abstract
Abstract By analyzing the optical band gap and energy levels of seven different regioregular terpolymers in which two different electron-rich donor moieties are alternating with a common electron-deficient acceptor unit along the backbone, we establish a direct correlation with the properties of the corresponding binary copolymers in which one donor and one acceptor are combined. For this study, we use diketopyrrolopyrrole as the common acceptor and different π-conjugated aromatic oligomers as donors. We find that the optical band gap and frontier orbital energies of the terpolymers are the arithmetic average of those of the parent copolymers with remarkable accuracy. The same relationship is also found for the open-circuit voltage of the bulk heterojunction solar cells made with the ter- and copolymers in combination with [6,6]-phenyl-C71-butyric acid methyl ester. Comparison of these findings with data in the literature suggests that this is a universal rule that can be used as a tool when designing new π-conjugated polymers. The experimental results are supported by a semiempirical quantum chemical model that accurately describes the energy levels of the terpolymers after parametrization on the energy levels of the copolymers and also provides a theoretical explanation for the observed arithmetic relations.
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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 | 0.001 |
| 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) | 0.004 | 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".