Plasmon-photon conversion to near-infrared emission from Yb3+: (Au/Ag-nanoparticles) in tungsten-tellurite glasses
Bibliographic record
Abstract
This manuscript reports on the interaction between (2)F5/2→(2)F7/2 radiative transition from Yb(3+) ions and localized surface plasmon resonance (from gold/silver nanoparticles) in a tungsten-tellurite glass. Such an interaction, similar to the down-conversion process, results in the Yb(3+) emission in the near-infrared region via resonant and non-resonant energy transfers. We associated such effects with the dynamic coupling described by the variations generated by the Hamiltonian HDC in either the oscillator strength, or the local crystal field, i.e. the line shape changes in the emission band. Here, the Yb(3+) ions emission is achieved through plasmon-photon coupling, observable as an enhancement or quenching in the luminescence spectra. Metallic nanoparticles have light-collecting capability in the visible spectrum and can accumulate almost all the photon energy on a nanoscale, which enable the excitation and emission of the Yb(3+) ions in the near-infrared region. This plasmon-photon conversion was evaluated from the cavity's quality factor (Q) and the coupling (g) between the nanoparticles and the Yb(3+) ions. We have found samples of low-quality cavities and strong coupling between the nanoparticles and the Yb(3+) ions. Our research can be extended towards the understanding of new plasmon-photon converters obtained from interactions between rare-earth ions and localized surface plasmon resonance.
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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".