Exploring copper nanostructures as highly uniform and reproducible substrates for plasmon-enhanced fluorescence
Bibliographic record
Abstract
The unique properties of metallic nanostructures of coinage metals that can sustain localized surface plasmon resonances (LSPR) put them at the centre of plasmon-enhanced phenomena. The theory of plasmonic phenomena based on LSPR is well-established. However, the fabrication of plasmonic substrates, reproducibly, is still challenging for applications in surface-enhanced Raman scattering (SERS) and surface-enhanced fluorescence (SEF). In this work we describe well-ordered copper nanostructures (CuNSs), produced by electrodeposition and nanosphere lithography, as active substrates for SEF. After a detailed spectroscopic and microscopic characterization, CuNSs are successfully applied as SEF-active substrates using a well-known perylene derivative as a target molecule. The signal reproducibility from CuNS substrates was established by comparing the results against those obtained from a simply roughened Cu substrate. Under optimal conditions, signal variability is around 4%.
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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 | 0.001 |
| 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".