Design and Computational Characterization of Non-Fullerene Acceptors for Use in Solution-Processable Solar Cells
Bibliographic record
Abstract
In an effort to seek high-performance small molecule electron acceptor materials for use in heterojunction solar cells, computational chemistry was used to examine a variety of terminal acceptor-conjugated bridge-core acceptor-conjugated bridge-terminal acceptor small molecules. In particular, we have systematically predicted the geometric, electronic, and optical properties of 16 potential small-molecule acceptors based upon a series of electron deficient π-conjugated building blocks that have been incorporated into materials exhibiting good electron transport properties. Results show that the band gap, HOMO/LUMO energy levels, orbital spatial distribution, and intrinsic dipole moments can be systematically altered by varying the electron properties of the terminal or core acceptor units. In addition, the identity of the conjugated bridge can help fine-tune the electronic properties of the molecule, where this study showed that the strongest electron affinity of the conjugated π-bridge increased the stability in the HOMO and LUMO energies and increased the band gap of these small-molecule acceptors. As a result, this work points toward an isoindigo (C5) core combined with C2-thienopyrrole dione (A5) terminal units as the most promising small molecule acceptor material that can be fine-tuned with the choice of conjugated bridge and may be considered as reasonable candidates for synthesis and incorporation into organic solar cells.
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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) | 0.002 | 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".