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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".