Bibliographic record
Abstract
The external quantum efficiencies (EQEs) of the state-of-the-art quantum dot light-emitting diodes (QD-LEDs) are limited by the out-coupling efficiency. Orienting the transition dipole moment (TDM), or in other words, making light emission directional, is a practically efficient approach to overcome this limitation. Nanoplates, nanorods, and dot-in-plate nanocrystals offer easily controllable TDM orientation; however, their low internal quantum efficiency (IQE) offset the gain in out-coupling, leading to no improvement in EQE. A joint team of researchers from the University of Science and Technology of China, Henan University and University of Toronto has developed isotropic-shaped QDs with directional light emission that does not compromise the IQE. These QDs feature a mixed crystallographic structure, i.e. they have both wurtzite and zinc blende phases in each single nanocrystal, which allow directional light emission at a single particle level. These QD also have strong internal dipole–dipole interaction that facilitates the alignment of light emission in ensemble films. Thanks to the enhanced photon out-coupling, the LED made from these QDs represents a peak EQE of 35.6%—far above the theoretical upper limit of devices using isotropic QD emitters.
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) | 0.002 | 0.001 |
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".