Microcavity effects in ensembles of silicon quantum dots coupled to high‐<i>Q</i> resonators
Bibliographic record
Abstract
Abstract Microcavities can be used to control the spectral properties of ensembles of quantum dots. In this work, spherical microcavities with quality (Q) factors as high as 3 × 108 in air were coated with a layer of fluorescent silicon quantum dots. After coating, the transmission Q factors decreased to approximately 106 or slightly lower, while the luminescence Q‐factors (QPL) reached a maximum of ∼4000. While these are the highest QPL values yet reported for Si QDs, QPL was always orders of magnitude lower than the intrinsic cavity Q. One reason for the difference has its origin in the quantum dot emission linewidth, which is associated only with the QPL and not with the cold cavity (intrinsic) Q. Essentially, the Q factor in luminescence experiments arises from the spontaneous emission rate enhancements or suppressions experienced by the dot in the cavity. We developed a general model to calculate the luminescence spectrum and decay dynamics for an inhomogeneously broadened ensemble of quantum dots with arbitrary emission linewidths weakly coupled to an optical cavity. Using this model and comparing it with experiment, we show that the respective values of the luminescence quality factors and the cold cavity quality factors can be used to determine an effective quantum dot emission linewidth without the need for single particle spectroscopy. In the case of Si quantum dots, the room temperature emission linewidth may be as small as a few meV at room temperature.
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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) | 0.000 | 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".