Energy Transfer between Size-Controlled CsPbI<sub>3</sub> Quantum Dots for Light-Emitting Diode Application
Bibliographic record
Abstract
Perovskite quantum dots (PQDs) are applicable in light-emitting diodes (LEDs) owing to their color tunability, high color purity, and excellent photoluminescence quantum yield (PLQY) in the solution state. However, a PQD film obtained through nonradiative recombination by concentration quenching and the formation of surface defects exhibited a low PLQY. In this study, we focused on the energy transfer between PQDs with different energy gaps ( E g ) to reduce nonradiative recombination in the film state and consequently achieve high device performance. We prepared size-controlled PQDs measuring 10.7 nm (large-size QD; LQD) and 7.9 nm (small-size QD; SQD) with different E g values and observed a spectral overlap between SQD emission and LQD absorption. To investigate the Förster resonance energy transfer (FRET) from SQDs to LQDs, we prepared SQD–LQD mixed QDs (MQDs). The MQD film enhanced LQD emission and exhibited a higher PLQY (52%) with a longer PL decay time (7.4 ns) than those exhibited by the neat LQD film (38% and 6.2 ns). This energy transfer was determined to be FRET by photoluminescence excitation and PL decay times. Moreover, the external quantum efficiency of an MQD-based LED increased to 15%, indicating that the FRET process can enhance the PLQY of the film and LED efficiency.
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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".