State-resolved manipulations of optical gain in semiconductor quantum dots: Size universality, gain tailoring, and surface effects
Bibliographic record
Abstract
Optical gain in strongly confined colloidal semiconductor quantum dots is measured using state resolved pump/probe spectroscopy. Though size tunable optical amplification has been previously reported for these materials, the influence of confinement enhanced multiexcitonic interactions has limited prior demonstrations to specific particle sizes or host media. Here we show that the influence of the interfering multiexcitonic interactions, and hence the development of optical gain, is dependent on the identity of the initially prescribed excitonic state. By maintaining a constant excitonic state in the size tunable electronic structure of these materials, we recover the predicted universal development of optical gain, reflected by size-independent occupation thresholds, and differential gains. In addition, we explicitly compare the influence of surface passivation on the development and lifetime of the optical gain. Furthermore, we introduce a general, state-resolved pumping scheme which enables control over the optical gain spectrum. The capacity to manipulate the optical gain spectra of these spherically confined systems is evident in both the measured stimulated emission and amplified spontaneous emission. We anticipate that state-resolved optical excitation will be a useful method of enabling the development and manipulation of optical gain in any quantized nanostructure.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".