Exceptionally stable quantum dot/siloxane hybrid encapsulation material for white light-emitting diodes with a wide color gamut
Bibliographic record
Abstract
Herein, we report a luminescent light-emitting diode (LED) encapsulating material using a thermally curable quantum dot (QD)/siloxane hybrid (TSE-QD) color converter, which has superior long-term stability even at elevated temperatures, in high humidity, and in various chemicals. The TSE-QD is cured by a thermal-induced hydrosilylation reaction of an in situ sol-gel synthesized QD dispersed siloxane resin (QD/siloxane resin) without additional ligand-exchange processes. QDs are successfully encapsulated by highly condensed and linear structured siloxane networks with additional chemical linkages between the surface ligands of the QDs and organic functional groups of the siloxane matrix. Moreover, QDs are uniformly distributed within the siloxane matrix retaining their optical properties during the fabrication processes of the TSE-QD. The result is that the stability, as evaluated by the photoluminescence (PL) quantum yield (QY), is greatly improved under harsh conditions, for example, 120 °C/5% relative humidity (RH), ethanol and acetone for 30 days. Based on the exceptionally stable TSE-QD, we demonstrate a white LED using a blue LED chip directly encapsulated by a yellow emitting TSE-QD that shows excellent spectral stability, outstanding reliability at 85 °C/85% RH and a wide color gamut (116% of NTSC).
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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".