Ag@Au Core–Shell Porous Nanocages with Outstanding SERS Activity for Highly Sensitive SERS Immunoassay
Bibliographic record
Abstract
A highly sensitive immunoassay of biomarkers has been achieved using 4-mercaptobenzoic acid-labeled Ag@Au core–shell porous nanocage tags and α-fetoprotein immuno-sensing chips. The Ag@Au porous nanocages were uniquely synthesized by using an Ag core as a self-sacrificial template and reducing agent, where the slow reaction process led to the formation of a porous Au layer. The size of the remaining Ag core and surface roughness of the Au shell were controlled by adjusting the chloroauric acid concentration. The porous cage exhibited excellent surface-enhanced Raman spectroscopy (SERS) activity, presumably due to a synergetic interaction between newly generated hot spots in the rough Au shell and the retained SERS activity of the Ag core. Using α-fetoprotein as a model analyte for immunoassay, the SERS signal had a wide linear range of 0.20 ng mL−1 to 500.0 ng mL−1 with a detection limit of 0.12 ng mL−1. Without the need of further signal amplification, the as-prepared Ag@Au bimetallic nanocages can be directly used for highly sensitive SERS assays of other biomarkers in biomedical research, diagnostics, etc.
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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 | 0.001 | 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".