Hybrid ZnO-Based Nanoconjugate for Efficient and Sustainable White Light Generation
Bibliographic record
Abstract
Developing new ways of generating white light is of paramount importance for the design of the next generation of smart, energy-efficient lighting sources. Here we report tunable white light emission of hybrid organic–inorganic nanostructures based on colloidal ZnO nanocrystals conjugated with organic fluorophores. These materials act as single nanophosphors owing to the distance-dependent energy transfer between the two components. The defect-based size-tunable ZnO nanocrystal blue-green emission coupled with complementary color emission from different fluorophores allows for the generation of white light with targeted chromaticity, color temperature, and color rendering index. We further show that silane layer-protected nanoconjugates result in increased stability of white light emission over a long period of time. The results of this work demonstrate an inexpensive, green, and sustainable approach to general solid-state lighting, without the use of rare earth or heavy metals. Colloidal form of the reported hybrid nanoconjugates allows for their further functionalization or incorporation into light-emitting devices. More broadly, size dependence of the electronic structure of native defects in transparent metal oxide nanocrystals and their electronic coupling with conjugated organic species could also represent a vehicle for introducing and manipulating new properties in these hybrid nanostructures.
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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".