High-Efficiency Circularly Polarized Light-Emitting Diodes Based on Chiral Metal Nanoclusters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Circularly polarized light-emitting diodes (CP-LEDs) are critical for next-generation optical technologies, ranging from holography to quantum information processing. Currently deployed chiral luminescent materials, with their intricate synthesis and processing and limited efficiency, are the main bottleneck for CP-LEDs. Chiral metal nanoclusters (MNCs) are potential CP-LED materials, given their ease of synthesis and processability as well as diverse structures and excited states. However, their films are usually plagued by inferior electronic quality and aggregation-caused photoluminescence quenching, necessitating their incorporation into host materials; without such a scheme, MNC-based LEDs exhibit external quantum efficiencies (EQEs) < 10%. Herein, we achieve an efficiency leap for both CP-LEDs and cluster-based LEDs by using novel chiral MNCs with aggregation-induced emission enhancement. CP-LEDs using enantiopure MNC films attain EQEs of up to 23.5%. Furthermore, by incorporating host materials, the devices yield record EQEs of up to 36.5% for both CP-LEDs and cluster-based LEDs, along with electroluminescence dissymmetry factors (| g EL |) of around 1.0 × 10 –3 . These findings open a new avenue for advancing chiral light sources for next-generation optoelectronics.
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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".