Observation and biomedical application of plasmon-enhanced fluorescence induced multiple stimulated Stokes Raman scattering in FITC-conjugated gold nanoparticles solution
Bibliographic record
Abstract
Abstract This work describes the observation of multi-wavelengths due to stimulated Raman scattering from deep red to near-infrared (NIR) induced by plasmon-enhanced fluorescence (PEF) of fluorescein isothiocyanate (FITC) conjugated gold nanoparticles (F-AuNP) excited by 488 nm diode laser. Coupling between the near-field of localized surface plasmons of AuNP with molecules amplifies the fluorescence signal i.e. brightness hence the quantum yield by several orders of magnitude. Resonance Rayleigh scattering and PEF conditions are satisfied due to the overlapping of 488 nm of the laser beam with the maximum absorbance of FITC, and the SPR of AuNP with FITC emission spectrum at ≈525 nm. PEF acts as Stoke sand secondary pump beam for further optical excitation. Surface-enhanced Raman scattering (SERS) showed FITC molecular bonds at 600, 1680, and a weak deviation at 1280 cm −1 , respectively. Longer wavelengths with higher output power were observed between 180° and 270°, and shorter wavelengths with lower power between 90° and 0° respectively. F-AuNPs were incubated and up taken by the oyster mushroom (OM) grown in the lab for bioimaging purposes and studied by phase-contrast microscope (PCM), and fluorescence microscope (FM). The FM results revealed visible colours, which can be utilized for in vitro and in vivo biosensing applications.
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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) | 0.000 | 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".