Recent Progress in Surface-Enhanced Fluorescence Using Gold Nanorods
Bibliographic record
Abstract
Plasmonic nanoparticles have been intensely used in research because they possess powerful optical properties. Gold nanorods (Au NRs), in particular, feature the interesting ability to absorb and scatter light in the near-infrared region through their longitudinal localized surface plasmon resonance. This property is particularly interesting in biology because these wavelengths are associated with maximum tissue penetration. The interplay between plasmonic nanoparticles and fluorophores has also triggered exciting research. Specifically, illuminating the Au NRs produces an enhanced electromagnetic field in the vicinity of the particles. This allows nearby fluorophores to feature enhanced optical properties through a phenomenon known as surface-enhanced fluorescence. This effect, however, must be counterbalanced by potential quenching that can happen from the Au NRs to the fluorophore. Systems have been developed whereby a spacer layer insulates the Au NR from the fluorophore. Multiple studies have explored different materials to use as the spacer layer such as silica or polyelectrolyte multilayers. Many of these studies have also investigated the optimal spacer thickness for achieving maximum enhancement. This review summarizes and provides an outlook on important developments in this field that have been reported in the last 10 years.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
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 teacher head, 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".