Magnetic Purcell Enhancement in a Nanoantenna‐Spherical Bragg Resonator Coupled System
Bibliographic record
Abstract
Abstract In this work, a novel approach for enhancing magnetic fields in all‐dielectric nanoantennas using Spherical Bragg Resonators (SBR) is proposed, which can boost quantum emitters' magnetic transitions. A matrix method has been used to optimize the magnetic dipole resonance of a SiO2/Si core‐shell spherical nanoantenna. The radiative and non‐radiative decay rate of a Eu3+ emitter with a quantum efficiency of ∼80% is studied. The findings revealed that the magnetic dipole nanoantenna resonance coupling with the SBR mode significantly enhances the modal magnetic field. A 4‐layer SiO2/Si SBR results in a Purcell factor of , the highest it has found in the literature, to the best of the knowledge. The work offers a theoretical demonstration of the potential of SBR to improve the performance of dielectric nanoantennas.
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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".