Giant Alloyed Hot Injection Shells Enable Ultralow Optical Gain Threshold in Colloidal Quantum Wells
Bibliographic record
Abstract
As an attractive materials system for high-performance optoelectronics, colloidal nanoplatelets (NPLs) benefit from atomic-level precision in thickness, minimizing emission inhomogeneous broadening. Much progress has been made to enhance their photoluminescence quantum yield (PLQY) and photostability. However, to date, layer-by-layer growth of shells at room temperature has resulted in defects that limit PLQY and thus curtail the performance of NPLs as an optical gain medium. Here, we introduce a hot-injection method growing giant alloyed shells using an approach that reduces core/shell lattice mismatch and suppresses Auger recombination. Near-unity PLQY is achieved with a narrow full-width-at-half-maximum (20 nm), accompanied by emission tunability (from 610 to 650 nm). The biexciton lifetime exceeds 1 ns, an order of magnitude longer than in conventional colloidal quantum dots (CQDs). Reduced Auger recombination enables record-low amplified spontaneous emission threshold of 2.4 μJ cm –2 under one-photon pumping. This is lower by a factor of 2.5 than the best previously reported value in nanocrystals (6 μJ cm –2 for CdSe/CdS NPLs). Here, we also report single-mode lasing operation with a 0.55 mJ cm –2 threshold under two-photoexcitation, which is also the best among nanocrystals (compared to 0.76 mJ cm –2 from CdSe/CdS CQDs in the Fabry–Pérot cavity). These findings indicate that hot-injection growth of thick alloyed shells makes ultrahigh performance NPLs.
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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".