Optical response of plasmonic silver nanoparticles after treatment by a warm microwave plasma jet
Bibliographic record
Abstract
Abstract This work investigates the effect of plasma treatment on the morphology and composition of 15 × 15 mm2 silver nanoparticle (70–80 nm) thin films. The silver nanoparticles are deposited onto thermal silica (SiO2/Si) substrates by spin-coating, then they are treated by an open-to-air microwave argon plasma jet characterized by a neutral gas temperature of 2200 ± 200 K. Scanning electron microscopy analysis reveals that the number of isolated nanoparticles in the film sample decreases after exposure to multiple jet passes, and that polygonal structures with sharp corners and edges are produced. Similar structures with much rounder edges are obtained after conventional thermal annealing at temperatures up to 1300 K. Based on localized surface plasmon resonance analysis in the range of 350–800 nm, the main extinction band of silver nanoparticles experiences a redshift after treatment with the plasma jet or with thermal annealing. Moreover, both treatments induce surface oxidation of the nanoparticles, as evidenced by x-ray photoelectron spectroscopy. However, only the plasma-exposed samples exhibit a significant rise in the surface-enhanced Raman scattering (SERS) signal of oxidized silver at 960 cm−1. 29 × 29 μm2 mappings of hyperspectral Raman IMAging (RIMA) and multivariate curve resolution analysis by log-likelihood maximization demonstrate that the SERS signal is controlled by large-scale micrometer domains that exhibit sharp corners and edges.
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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".