Influence of the quantum dots bandgap and their dispersion on the loss of luminescent quanta
Bibliographic record
Abstract
The method for estimating the loss of luminescent quanta caused by reabsorption has been proposed. The method is based on the analysis of absorption and luminescence spectra of quantum dots (QDs) with different radii and dispersion of radius . The loss was estimated for QDs of six semiconductor materials with different bulk bandgap Eg0: CdS (Eg0 = 2.42 eV), CdSe (Eg0 = 1.74 eV), CdTe (Eg0 = 1.56 eV), InP (Eg0 = 1.34 eV), InAs (Eg0 = 0.36 eV), and PbSe (Eg0 = 0.27 eV). It has been ascertained that, by changing and , one can find the optimal values of these parameters, for which the losses of luminescent quanta are minimal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".