Dipole Engineering through the Orientation of Interface Molecules for Efficient InP Quantum Dot Light-Emitting Diodes
Bibliographic record
Abstract
InP-based quantum dot (QD) light-emitting diodes (QLEDs) provide a heavy-metal-free route to size-tuned LEDs having high efficiency. The stability of QLEDs may be enhanced by replacing organic hole-injection layers (HILs) with inorganic layers. However, inorganic HILs reported to date suffer from inefficient hole injection, the result of their shallow work functions. Here, we investigate the tuning of the work function of nickel oxide (NiO x ) HILs using self-assembled molecules (SAMs). Density functional theory simulations and near-edge X-ray absorption fine structure put a particular focus onto the molecular orientation of the SAMs in tuning the work function of the NiO x HIL. We find that orientation plays an even stronger role than does the underlying molecular dipole itself: SAMs having the strongest electron-withdrawing nitro group (NO 2 ), despite having a high intrinsic dipole, show limited work function tuning, something we assign to their orientation parallel to the NiO x surface. We further find that the NO 2 group─which delocalizes electrons over the molecule by resonance─induces a deep lowest unoccupied molecular orbital level that accepts electrons from QDs, producing luminescence quenching. In contrast, SAMs containing a trifluoromethyl group exhibit an angled orientation relative to the NiO x surface, better activating hole injection into the active layer without inducing luminescence quenching. We report an external quantum efficiency (EQE) of 18.8%─the highest EQE among inorganic HIL-based QLEDs (including Cd-based QDs)─in InP QLEDs employing inorganic HILs.
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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".