On the Carrier Injection Efficiency and Thermal Property of InGaN/GaN Axial Nanowire Light Emitting Diodes
Bibliographic record
Abstract
We have investigated the impact of surface recombination on the effective carrier injection efficiency and the Joule heating of axial InGaN/GaN nanowire light-emitting diodes (LEDs). The results reveal that the carrier injection efficiency of such devices is extremely low (<;10%), due to the severe carrier loss through nonradiative surface recombination. It is further observed that the thermal resistance of typical nanowire LEDs is comparable with, or lower than that of their planar counterparts, in spite of the reduced thermal conductivity of nanowires. The poor carrier injection efficiency, however, leads to significantly elevated junction temperatures for nanowire LEDs. We have further demonstrated, both theoretically and experimentally, that the carrier injection efficiency can be significantly improved in p-doped nanowires, due to the downward surface band bending, and in InGaN/GaN/AlGaN dot-in-a-wire core-shell nanoscale heterostructures, due to the superior carrier confinement offered by the large bandgap AlGaN shell. This paper offers important insight for the design and epitaxial growth of high-performance nanowire LEDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".