The potential benefits of polyethylenimine as an electron extraction layer for facilitating the manufacturing of inverted organic solar cells
Bibliographic record
Abstract
The solvent-robustness and temporal stability of polyethylenimine (PEI) as an electron extraction layer (EEL) in inverted organic solar cells (OSCs) were studied. For that purpose, a PEI EEL is utilized in inverted OSCs with the archetypal Poly (3-hexylthiophene) (P3HT): [6,6]-Phenyl C61 butyric acid methyl ester (PC60BM) donor:acceptor system. Results show that soaking the PEI film in solvents (1-propanol and/or toluene) does not significantly impact OSC performance or photostability. As verified by X-ray photoelectron spectroscopy (XPS) measurements, the N atoms in PEI interact with indium-tin-oxide (ITO), causing PEI to strongly adhere to the surface of ITO so that potential processing solvents do not dissolve it. Shifts in N bands in the case of PEI on ITO compared to the PEI on glass confirm the presence of a strong physical interaction. In addition, comparing OSCs with fresh PEI and N2-stored PEI demonstrates that the PEI film is very stable over time, and a time gap between PEI deposition and subsequent fabrication processes does not affect OSC performance and photostability. We highlight that the utilization of PEI as a stable and robust EEL facilitates bridging between laboratory discoveries of OSCs with their practical demonstration and gives us considerable latitude in tackling the stringent requirements of OSC manufacturing.
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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) | 0.000 | 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".