Slot‐Die Coating of All Organic/Polymer Layers for Large‐Area Flexible OLEDs: Improved Device Performance with Interlayer Modification
Bibliographic record
Abstract
Abstract Organic light‐emitting diodes (OLEDs) fabricated on flexible transparent substrates have found application in displays, lighting, and signage. Optimization of the fabrication procedures is critical for commercialization to maximize performance and reduce costs. Utilization of the slot‐die coating technique, a solution‐processing roll‐to‐roll method, has emerged as a viable method to manufacture flexible OLED devices. Here, the fabrication of flexible OLEDs via the slot‐die coating technique under ambient condition is reported. The OLEDs have a simple architecture of polyethylene terephthalate (PET)/indium–tin oxide (ITO)/hole injection layer (HIL)/emissive layer (EML)/electron injection layer (EIL)/Ag, where poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) is used as the HIL, Super Yellow (SY, poly(1,4‐phenylenevinylene) copolymer) is used as the EML, and poly((9,9‐bis(3′‐(N,N‐dimethylamino)propyl)‐2,7‐fluorene)‐alt‐2,7‐(9,9‐dioctylfluorene)) (PFN) is used as the EIL. The optimization of the PFN layer using slot‐die coating technique is focused by varying solvent, solvent additives, substrate temperature, and coating speeds. The OLEDs that have PFN layers processed with the known solvent additives, 1,8‐diiodooctane (DIO) or diphenyl ether (DPE), show a performance increase compared to OLEDs based on PFN layers processed with no solvent additives. Finally, large‐area multicolor OLEDs are constructed with organic/polymer layers being slot‐die coated in sequence, of which all display homogeneous luminance under bending/folding conditions, highlighting the potential of this simple device structure and processing method.
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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) | 0.001 | 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".