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 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 | 0.000 | 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 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".