Combining Facile Synthetic Methods with Greener Processing for Efficient Polymer‐Perylene Diimide Based Organic Solar Cells
Bibliographic record
Abstract
Abstract Through ease of scalability and facile synthetic methods, eight N‐annulated perylene diimide dimers with different aliphatic chains are synthesized and evaluated as non‐fullerene acceptors in organic solar cells (OSCs). Optical absorption and emission spectroscopy, and cyclic voltammetry are used to characterize the materials. Variation of the length and topology of the aliphatic chains attached at the pyrrolic N‐position is shown to have minimal effect on properties in solution. As films, the use of a branched aliphatic chains results in the dimer exhibiting a low energy shoulder in the absorption spectrum and a narrower emission band. OSCs are fabricated and tested in air, at room temperature, using an inverted architecture. The polymer PTB7‐Th is used as the donor and all active layers are processed from 2‐methyltetrahydrofuran. OSC power conversion efficiencies are shown to vary from 3.7%–5.4% for OSCs. The dimer with 2‐ethylhexyl aliphatic chains is selected for optimization because of a high organic solvent solubility and excellent film formation properties. Use of the solvent additive 1,8‐diiodooctane during film formation lead to an increase in efficiency to 6.6%, while halogen‐free processed OSCs reach 6%. This result offers a simple method for materials side‐chain engineering for the development of OSCs processed in air from eco friendly solvents.
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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) | 0.001 | 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".