Quantum Dots on Optical Nanofiber Tips: A Hybrid Platform for Quantum Photonics
Bibliographic record
Abstract
We experimentally demonstrate fluorescence emission coupling from quantum dots (QDs) into optical nanofiber tip (ONFT) guided modes and the polarization response of the coupled system. Additionally, we conduct simulations of the proposed system consisting of the ONFT and a single dipole source (SDS). For a radially oriented SDS of emission wavelength at 620 nm, positioned at the center of the ONFT facet, the maximum coupling efficiency$(\eta)$reaches 44%. The polarization response for the SDS on the side and the facet of the ONFT are numerically investigated, and the degree of polarization (DOP) determined on the facet/side is 99%/52%. ONFTs are realized using chemical etching technique using hydrofluoric acid. QDs are deposited on ONFTs using a micro/nano-fluidic technique. QDs on the ONFT are excited using excitation schemes such as free space and guided. The fluorescence photons emitted from QDs are coupled into guided modes of the ONFT. It is confirmed by measuring the fluorescence photon counting and emission spectrum. The emission polarization characteristics of the fluorescence photons are measured. The polarization characteristics are demonstrated by varying the emission and excitation polarization planes, and the DOP is determined. The estimated DOP value ranges from 17 to 30%.
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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) | 0.001 | 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".