Regioisomerically Pure 1,7-Dicyanoperylene Diimide Dimer for Charge Extraction from Donors with High Electron Affinities
Bibliographic record
Abstract
Abstract Perylene diimide (PDI) has attracted widespread interest as an inexpensive electron acceptor for photovoltaic applications; however, overcrystallization in the bulk heterojunction typically leads to low device performance. Recent work has addressed this issue by forming bay-linked PDI dimers and oligomers, where the steric bulk of adjacent PDI units forces the molecule to adopt a nonplanar structure. This disrupts the molecular packing and limits domain sizes in the bulk heterojunction. Unfortunately, the introduction of electron-donating/-withdrawing groups in the bay region is also the best way to fine-tune the frontier molecular orbitals (FMOs) of PDI, which is highly desirable from a device optimization standpoint. This competition for the bay region has made it difficult for PDI to keep pace with other non-fullerene acceptors. Here, we report the synthesis of regioisomerically pure 1,7-dicyanoperylene diimide and its dimerization through an imide linkage. We show that this is an effective strategy to tune the energies of the FMOs while simultaneously suppressing overcrystallization in the bulk heterojunction. The resulting acceptor has a low LUMO energy of −4.2 eV and is capable of accepting photogenerated electrons from donor polymers with high electron affinities, even when conventional acceptors such as PDI, PC71BM, and ITIC cannot.
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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) | 0.004 | 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".