Biofunctionalization of Plasmonic Nanoparticles with Short Peptides Monitored by SERS
Bibliographic record
Abstract
In order for plasmonic nanoparticles to be usable in biomedical applications their surface requires functionalization with biocompatible material. For this purpose short peptides, CFY, CFFY, CLY, were designed and replacement of the capping agent poly(vinylpyrrolidone) (PVP) on the surface of silver nanocubes by the peptides was investigated. The primary sequences of the peptides were designed such that they enable the covalent attachment to silver via the cysteine thiols, contain amino acids that can interact via hydrophobic interactions, and therefore are likely to form tightly packed films. Finally, the peptides contained UV–vis and SERS markers, allowing the dynamics of the biomolecule attachment to the nanoparticles to be monitored spectroscopically. The ligand exchange was observed for nanocubes suspended in solution and supported on a dielectric substrate. Formation of the peptide film around the nanocubes was confirmed by electron microscopy and SERS measurements. The film thickness was found to be 4–6 nm and independent of peptide solution concentration, suggesting multilayer formation. The surface density of these cysteine-containing peptides was found to be between 0.59 and 4.92 molecules per nm 2 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".