Separating Crystal Growth from Nucleation Enables the In Situ Controllable Synthesis of Nanocrystals for Efficient Perovskite Light‐Emitting Diodes
Bibliographic record
Abstract
Abstract Colloidal perovskite nanocrystals (PNCs) display bright luminescence for light‐emitting diode (LED) applications; however, they require post‐synthesis ligand exchange that may cause surface degradation and defect formation. In situ‐formed PNCs achieve improved surface passivation using a straightforward synthetic approach, but their LED performance at the green wavelength is not yet comparable with that of colloidal PNC devices. Here, it is found that the limitations of in situ‐formed PNCs stem from uncontrolled formation kinetics: conventional surface ligands confine perovskite nuclei but fail to delay crystal growth. A bifunctional carboxylic‐acid‐containing ammonium hydrobromide ligand that separates crystal growth from nucleation is introduced, leading to the formation of quantum‐confined PNC solids exhibiting a narrow size distribution. Controlled crystallization is further coupled with defect passivation using deprotonated phosphinates, enabling improvements in photoluminescence quantum yield to near unity. Green LEDs are fabricated with a maximum current efficiency of 109 cd A−1 and an average external quantum efficiency of 22.5% across 25 devices, exceeding the performance of their colloidal PNC‐based counterparts. A 45.6 h operating half‐time is further documented for an unencapsulated device in N2 with an initial brightness of 100 cd m−2.
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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".