Portable smart films for ultrasensitive detection and chemical analysis using SERS and SERRS
Bibliographic record
Abstract
Metallic nanostructures, much smaller than the wavelength of visible light, which support localized surface plasmon resonances, are central to the giant signal enhancement achieved in surface‐enhanced Raman scattering (SERS) and surface‐enhanced resonance Raman scattering (SERRS). Plasmonic driven SERS and SERRS is a powerful analytical tool for ultrasensitive detection down to single molecule detection. For all practical SERS applications a key issue is the development of reproducible and portable SERS‐active substrates, where the most widely used metals for nanostructure fabrication are silver and gold. Here, we report the fabrication of a ‘smart film’, containing gold nanoparticles (AuNPs), produced by in situ reduction of gold chloride III (Au+3) in natural rubber (NR) membranes for SERS and SERRS applications. The composite films (NR/AuNP membranes) show characteristic plasmon absorption of Au nanostructures, which notably do not influence the mechanical properties of the NR membranes. The term ‘smart film’ has to do with the fact that the SERS substrate (smart film) is flexible and standalone, which allows one to take it anywhere and to dip it into solutions containing the analyte to be characterized by SERS or SERRS technique. Besides, the synthesis of the AuNPs at the surface of NR films is much simpler than making an Au colloid and cast it onto a substrate surface or preparing an Au evaporated film. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".