A Simple Approach to Engineer SERS Substrates and Plasmonic Thin Film
Bibliographic record
Abstract
Solid supported 2D assembly of silver nanocubes was fabricated by Langmuir-Blodgett technique and employed to investigate its surface enhanced Raman scattering (SERS) and surface enhance fluorescence (SEF) activities by detecting Rh6G in solution of varied concentrations, that is, 10−12 M, 10−9 M, 10−6 M, and 10−3 M. SERS was detected from a nanomolar concentration of Rh6G whereas SEF was detected from a picomolar concentration. Further, the substrate was subjected to thermal annealing to fabricate plasmonic thin film. The formation of thin film was followed by monitoring its surface plasmon resonance spectra and atomic force microscopic images. It was observed that the characteristic spectral peaks of silver nanocubes merged into a broad spectral band as the annealing time was increased and the intensity of the band decreased with the formation of thin film. The obtained result implies that thermal annealing could be a simple approach to create nanoscale gaps in SERS substrate and to engineer continuous thin film from the assembly of discrete 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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".