A spectrally tunable all-graphene-based flexible field-effect light-emitting device
Bibliographic record
Abstract
The continuous tuning of the emission spectrum of a single light-emitting diode (LED) by an external electrical bias is of great technological significance as a crucial property in high-quality displays, yet this capability has not been demonstrated in existing LEDs. Graphene, a tunable optical platform, is a promising medium to achieve this goal. Here we demonstrate a bright spectrally tunable electroluminescence from blue (∼450 nm) to red (∼750 nm) at the graphene oxide/reduced-graphene oxide interface. We explain the electroluminescence results from the recombination of Poole-Frenkel emission ionized electrons at the localized energy levels arising from semi-reduced graphene oxide, and holes from the top of the π band. Tuning of the emission wavelength is achieved by gate modulation of the participating localized energy levels. Our demonstration of current-driven tunable LEDs not only represents a method for emission wavelength tuning but also may find applications in high-quality displays.
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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 | 0.001 | 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".