Improving the stability of organic light-emitting devices by using a hole-injection-tunable-anode-buffer-layer
Bibliographic record
Abstract
Introducing a hole-injection-tunable-anode-buffer-layer (HITABL) at the indium tin oxide anode contact of an organic light-emitting device can finely tune hole injection to establish proper charge balance, thus remarkably improves its operational stability. The HITABL consists of two sublayers: (i) an ∼2.5nm thick metal (e.g., Ca, Mg, or Ag) sublayer and (ii) an ∼10nm thick tetrafluorotetracyanoquinodimethane (F4TCNQ) doped N′-di(naphthalene-1-yl)-N,N′-diphenyl-benzidine sublayer. Hole injection can be tuned by changing (i) the metal in the first sublayer and/or (ii) the concentration of the F4TCNQ dopant in the second sublayer. The choice of the metal used in the first sublayer and/or the concentration of F4TCNQ in the second sublayer affect the hole-injection efficiency. Therefore, by using the HITABL, one can make the necessary diminutive adjustments to the hole injection of a device and achieve proper charge balance, resulting in a significant improvement in operational stability.
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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 | 0.001 | 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".