Improvement of the light extraction efficiency of GaN-based LEDs using rolled-up nanotube arrays
Bibliographic record
Abstract
In this paper, we have investigated the effect of rolled-up nanotubes on the light extraction efficiency of GaN-based LEDs using two-dimensional finite element method simulation. The light extraction involves two successive steps, including the coupling from the light source to the tube and the subsequent emission from the tube to the air. Significantly enhanced light extraction efficiency is observed for both TE and TM waves by optimizing the nanotube geometry and dimension as well as the separation between the nanotube and light source. We have further shown that densely packed nanotube arrays can be integrated with GaN-based LEDs to achieve unequivocal improvement of light extraction efficiency over a large surface area. With recent advances in rolled-up micro- and nanotubes, it is expected that this study can offer a potentially flexible, low cost approach to enhance the light extraction of various LED devices.
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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 | 0.000 | 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 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".