Quantum Dot Self‐Assembly Enables Low‐Threshold Lasing
Bibliographic record
Abstract
Abstract Perovskite quantum dots (QDs) are of interest for solution‐processed lasers; however, their short Auger lifetime has limited lasing operation principally to the femtosecond temporal regime the photoexcitation levels to achieve optical gain threshold are up to two orders of magnitude higher in the nanosecond regime than in the femtosecond. Here the authors report QD superlattices in which the gain medium facilitates excitonic delocalization to decrease Auger recombination and in which the macroscopic dimensions of the structures provide the optical feedback required for lasing. The authors develope a self‐assembly strategy that relies on sodiumd—an assembly director that passivates the surface of the QDs and induces self‐assembly to form ordered three‐dimensional cubic structures. A density functional theory model that accounts for the attraction forces between QDs allows to explain self‐assembly and superlattice formation. Compared to conventional organic‐ligand‐passivated QDs, sodium enables higher attractive forces, ultimately leading to the formation of micron‐length scale structures and the optical faceting required for feedback. Simultaneously, the decreased inter‐dot distance enabled by the new ligand enhances exciton delocalization among QDs, as demonstrated by the dynamically red‐shifted photoluminescence. These structures function as the lasing cavity and the gain medium, enabling nanosecond‐sustained lasing with a threshold of 25 µJ cm–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".