Bright Surface‐Enhanced Raman Scattering with Fluorescence Quenching from Silica Encapsulated J‐Aggregate Coated Gold Nanoparticles
Bibliographic record
Abstract
Abstract Plexitonic nanoparticles offer variable optical properties through tunable excitations, in addition to electric field enhancements that far exceed molecular resonators. This study demonstrates a way to design an ultrabright surface‐enhanced Raman spectroscopy (SERS) signal while simultaneously quenching the fluorescence background through silica encapsulation of the semiconductor–metal composite nanoparticles. Using a multistep approach, a J‐aggregate‐forming organic dye is assembled on the surface of gold nanoparticles using a cationic linker. Excitonic resonance of the J‐aggregate–metal system shows an enhanced SERS signal at an appropriate excitation wavelength. Further encapsulation of the decorated particles in silica shows a significant reduction in the fluorescence signal of the Raman spectra (5× reduction) and an increase in Raman scattering (7× enhancement) when compared to phospholipid encapsulation. This reduction in fluorescence is important for maximizing the useful SERS enhancement from the particle, which shows a signal increase on the order of 104 times greater than J‐aggregated dye in solution and 24 times greater than Oxonica S421 SERS tag. The silica layer also serves to promote colloidal stability. The combination of reduced fluorescence background, enhanced SERS intensity, and temporal stability makes these particles highly distinguishable with potential to enable high‐throughput applications such as SERS flow cytometry.
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) | 0.000 | 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".