Control of fluorescence in quantum emitter and metallic nanoshell hybrids for medical applications
Bibliographic record
Abstract
We study the light emission from a quantum emitter and double metallic nanoshell hybrid systems. Quantum emitters act as local sources which transmit their light efficiently due to a double nanoshell near field. The double nanoshell consists of a dielectric core and two outer nanoshells. The first nanoshell is made of a metal, and the second spacer nanoshell is made of a dielectric material or human serum albumin. We have calculated the fluorescence emission for a quantum emitter-double nanoshell hybrid when it is injected in an animal or a human body. Surface plasmon polariton resonances in the double nanoshell are calculated using Maxwell's equations in the quasi-static approximation, and the fluorescence emission is evaluated using the density matrix method in the presence of dipole-dipole interactions. We have compared our theory with two fluorescence experiments in hybrid systems in which the quantum emitter is Indocyanine Green or infrared fluorescent molecules. The outer spacer nanoshell of double metallic nanoshells consists of silica and human serum albumin with variable thicknesses. Our theory explains the enhancement of fluorescence spectra in both experiments. We find that the thickness of the spacer nanoshell layer increases the enhancement when the fluorescence decreases. The enhancement of the fluorescence depends on the type of quantum emitter, spacer layer, and double nanoshell. We also found that the peak of the fluorescence spectrum can be shifted by changing the shape and the size of the nanoshell. The fluorescence spectra can be switched from one peak to two peaks by removing the degeneracy of excitonic states in the quantum emitter. Hence, using these properties, one can use these hybrids as sensing and switching devices for applications in medicine.
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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".