Spectroscopic study of white organic light-emitting devices with various thicknesses of emissive layer
Bibliographic record
Abstract
White light-emitting devices based on a donor-acceptor structure were fabricated in order to investigate the dependence of the optical properties of white light emission on the thickness variance (15, 20, 25, and 30 nm) of the emissive layer. The emissive layer has a donor-acceptor system with the host 4,4′,4′′-tris(carbasol-l-nyl)triphenylamine molecule doped with 4,4′-bis(9-ethyl-3-carbazovinylene)-1,1′-biphenyl (BCzVBi) and 5,6,11,12-tetraphenylnaphtacene (Rubrene) molecules for blue and yellowish-green light activators, respectively. The characteristics of current density were analyzed by using a power function of applied field, J=σlEl+1 and the characteristic exponential function, J=J0(e(V-Vd)/V0-1). Through spectroscopic analysis, we obtained three physical quantities governing the device performance: 1) an effective conductivity, 2) a threshold potential, and 3) a characteristic potential barrier, which are associated with the trap-charge limited concentration in the bulk layer, the energy gap of the organic materials, and the barrier energy at the contact of electrodes, respectively. The electroluminescent spectra were studied quantitatively using a multi-peak fit with a Gaussian distribution for each electromagnetic transition. By this approach, we deduced the energy levels of the BCzVBi and Rubrene molecules that give leading information on the light emission mechanism and the energy transfer in the host-dopant system.
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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".