Optical quantum yield in plasmonic nanowaveguide
Bibliographic record
Abstract
We have developed a theory of the quantum yield for plasmonic nanowaveguide where the cladding layer is made of an ensemble of quantum dots and the core layer consists of an ensemble of metallic nanoparticles. The bound states of the confined probe photons in the plasmonic nanowaveguide are calculated using the transfer matrix method based on the Maxwell equations. It is shown that the number of bound states in the nanowaveguide depends on the dielectric properties of the core and cladding layers. The surface plasmon polaritons (SPPs) produced by the metallic nanoparticles interacts with the excitons of the quantum dots. The radiative and nonradiative linewidths of excitons in the quantum yield are calculated using the quantum mechanical perturbation theory. We have found that the quantum yield decreases as the dipole-dipole interaction between metallic nanoparticles increases. We have also calculated the photoluminescence and found that the enhancement in photoluminescence is due to the SPPs coupling. On the other hand, the quenching in the photoluminescence is due to the quantum yield. We compared our theory with experiments of a nanowaveguide where the core is fabricated from Ag- nanoparticles and the cladding is fabricated from the perovskite quantum dots. A good agreement between theory and experiments is found. Our analytical expressions of the quantum yield and photoluminescence can be used by experimentalists to proforma new types of experiments and for inventing new types of nanosensors and nanoswitches.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".