Shape-dependent surface-enhanced Raman scattering in gold–Raman-probe–silica sandwiched nanoparticles for biocompatible applications
Bibliographic record
Abstract
To meet the requirement of Raman probes (labels) for biocompatible applications, a synthetic approach has been developed to sandwich the Raman-probe (malachite green isothiocyanate, MGITC) molecules between the gold core and the silica shell in gold-SiO₂ composite nanoparticles. The gold-MGITC-SiO₂ sandwiched structure not only prevents the Raman probe from leaking out but also improves the solubility of the nanoparticles in organic solvents and in aqueous solutions even with high ionic strength. To amplify the Raman signal, three types of core, gold nanospheres, nanorods and nanostars, have been chosen as the substrates of the Raman probe. The effect of the core shape on the surface-enhanced Raman scattering (SERS) has been investigated. The colloidal nanostars showed the highest SERS enhancement factor while the nanospheres possessed the lowest SERS activity under excitation with 532 and 785 nm lasers. Three-dimensional finite-difference time domain (FDTD) simulation showed significant differences in the local electromagnetic field distributions surrounding the nanospheres, nanorods, and nanostars, which were induced by the localized surface plasmon resonance (LSPR). The electromagnetic field was enhanced remarkably around the two ends of the nanorods and around the sharp tips of the nanostars. This local electromagnetic enhancement made the dominant contribution to the SERS enhancement. Both the experiments and the simulation revealed the order nanostars > nanorods > nanospheres in terms of the enhancement factor. Finally, the biological application of the nanostar-MGITC-SiO₂ nanoparticles has been demonstrated in the monitoring of DNA hybridization. In short, the gold–MGITC-SiO₂ sandwiched nanoparticles can be used as a Raman probe that features high sensitivity, good water solubility and stability, low-background fluorescence, and the absence of photobleaching for future biological 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) | 0.000 | 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".