Porous Au–Ag Nanoparticles from Galvanic Replacement Applied as Single‐Particle SERS Probe for Quantitative Monitoring
Bibliographic record
Abstract
Plasmonic nanostructures have raised the interest of biomedical applications of surface-enhanced Raman scattering (SERS). To improve the enhancement and produce sensitive SERS probes, porous Au-Ag alloy nanoparticles (NPs) are synthesized by dealloying Au-Ag alloy NP-precursors with Au or Ag core in aqueous colloidal environment through galvanic replacement reaction. The novel designed core-shell Au-Ag alloy NP-precursors facilitate controllable synthesis of porous nanostructure, and dealloying degree during the reaction has significant effect on structural and spectral properties of dealloyed porous NPs. Narrow-dispersed dealloyed NPs are obtained using NPs of Au/Ag ratio from 10/90 to 40/60 with Au and Ag core to produce solid core@porous shell and porous nanoshells, having rough surface, hollowness, and porosity around 30-60%. The clean nanostructure from colloidal synthesis exhibits a redshifted plasmon peak up to near-infrared region, and the large accessible surface induces highly localized surface plasmon resonance and generates robust SERS activity. Thus, the porous NPs produce intensely enhanced Raman signal up to 68-fold higher than 100 nm AuNP enhancement at single-particle level, and the estimated Raman enhancement around 7800, showing the potential for highly sensitive SERS probes. The single-particle SERS probes are effectively demonstrated in quantitative monitoring of anticancer drug Doxorubicin release.
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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".