Silver Nanowire Layer-by-Layer Films as Substrates for Surface-Enhanced Raman Scattering
Bibliographic record
Abstract
In this paper, the fabrication of highly stable, surface-enhanced Raman scattering (SERS) active dendrimer/silver nanowire layer-by-layer (LBL) films is reported. Ag nanowires, approximately 100 nm in diameter, were produced in solution and transferred, using the LBL technique, onto a single fifth-generation DAB-Am dendrimer layer on a glass substrate. The Ag nanowires, and the resulting LBL films were characterized using UV-visible surface plasmon absorbance, while the LBL films were further characterized by atomic force microscopy measurements and surface-enhanced Raman and resonance Raman scattering of several analytes. The dendrimer was found to effectively immobilize the Ag nanowires with increased control over spacing and aggregation of the particles. These films are shown to be excellent substrates for SERS/SERRS measurements, demonstrating significant enhancement, and trace detection capability. Several trial analytes were tested using a variety of excitation energies, and results confirmed effective enhancement of Raman signals throughout the visible range (442-785 nm) with different molecules. Analytes were deposited onto the enhancing Ag nanowire LBL films surface using both casting and Langmuir-Blodgett monolayer transferring techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".