Tailored SERS substrates obtained with cathodic arc plasma ion implantation of gold nanoparticles into a polymer matrix
Bibliographic record
Abstract
This manuscript reports on the fabrication of plasmonic substrates using cathodic arc plasma ion implantation, in addition to their performance as SERS substrates. The technique allows for the incorporation of a wide layer of metallic nanoparticles into a polymer matrix, such as PMMA. The ability to pattern different structures using the PMMA matrix is one of the main advantages of the fabrication method. This opens up new possibilities for obtaining tailored substrates with enhanced performance for SERS and other surface-enhanced spectroscopies, as well as for exploring the basic physics of patterned metal nanostructures. The architecture of the SERS-active substrate was varied using three adsorption strategies for incorporating a laser dye (rhodamine): alongside the nanoparticles into the polymer matrix, during the polymer cure and within nanoholes lithographed on the polymer. As a proof-of-concept, we obtained the SERS spectra of rhodamine for the three types of substrates. The hypothesis of incorporation of rhodamine molecules into the polymer matrix during the cathodic arc plasma ion implantation was supported by FDTD (Finite-Difference Time-Domain) simulations. In the case of arrays of nanoholes, rhodamine molecules could be adsorbed directly on the gold surface, then yielding a well-resolved SERS spectrum for a small amount of analyte owing to the short-range interactions and the large longitudinal field component inside the nanoholes. The results shown here demonstrate that the approach based on ion implantation can be adapted to produce reproducible tailored substrates for SERS and other surface-enhanced spectroscopies.
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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 | 0.000 |
| 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".