Can Ferroelectricity Improve Organic Solar Cells?
Bibliographic record
Abstract
Abstract Blends of semiconducting (SC) and ferroelectric (FE) polymers have been proposed for applications in resistive memories and organic photovoltaics (OPV). For OPV, the rationale is that the local electric field associated with the dipoles in a blend could aid exciton dissociation, thus improving power conversion efficiency. However, FE polymers either require solvents or processing steps that are incompatible with those required for SC polymers. To overcome this limitation, SC (poly(3‐hexylthiophene)) and FE (poly(vinylidene fluoride‐trifluoroethylene)) components are incorporated into a block copolymer and thus a path to a facile fabrication of smooth thin films from suitably chosen solvents is achieved. In this work, the photophysical properties and device performance of organic solar cells containing the aforementioned block copolymer consisting of poly(vinylidene fluoride‐trifluoroethylene): P(VDF‐TrFE), poly(3‐hexylthiophene): P3HT and the electron acceptor phenyl‐C61‐butyric acid methyl ester: [60]PCBM are explored. A decrease in photovoltaic performance is observed in blends of the copolymer with P3HT:[60]PCBM, which is attributed to a less favorable nanomorphology upon addition of the copolymer. The role of lithium fluoride (the cathode modification layer) is also clarified in devices containing the copolymer, and it is demonstrated that ferroelectric compensation prevents the ferroelectricity of the copolymer from improving photovoltaic performance in SC‐FE blends.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".