Plasmonic emission enhancement of colloidal quantum dots in the presence of bimetallic nanoparticles
Bibliographic record
Abstract
We studied plasmonic features of bimetallic nanostructures consisting of gold nanoisland cores semi-coated with a chromium layer and explored how they influence emission of CdSe/ZnS quantum dots. We showed that, compared with chromium-covered glass substrates without the gold cores, the bimetallic nanostructures could significantly enhance the emission of the quantum dots. We studied the impact of the excitation intensity and thickness of the chromium layer on this process and utilized numerical means to identify the mechanisms behind it. Our results suggest that when the chromium layer is thin, the enhancement process is the result of the bimetallic plasmonic features of the nanostructures. As the chromium layer becomes thick, the impact of the gold cores is screened and the enhancement mostly happens mostly via the field enhancement of chromium nanoparticles in the absence of significant energy transfer from the quantum dots to these nanoparticles.
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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".