Silica Shell/Gold Core Nanoparticles: Correlating Shell Thickness with the Plasmonic Red Shift upon Aggregation
Bibliographic record
Abstract
Differences in the wavelengths of the surface plasmon band of gold nanoparticles (AuNP)--before and after particle aggregation--are widely used in bioanalytical assays. However, the gold surfaces in such bioassays can suffer from exchange and desorption of noncovalently bound ligands and from nonspecific adsorption of biomolecules. Silica shells on the surfaces of the gold can extend the available surface chemistries for bioconjugation and potentially avoid these issues. Therefore, silica was grown on gold surfaces using either hydrolysis/condensation of tetraethyl orthosilicate 1 under basic conditions or diglyceroxysilane 2 at neutral pH. The former precursor permitted slow, controlled growth of shells from about 1.7 to 4.3 nm thickness. By contrast, 3-4 nm thick silica shells formed within an hour using diglyceroxysilane; thinner or thicker shells were not readily available. Within the range of shell thicknesses synthesized, the presence of a silica shell on the gold nanoparticle did not significantly affect the absorbance maximum (~5 nm) of unaggregated particles. However, the change in absorbance wavelength upon aggregation of the particles was highly dependent on the thickness of the shell. With silica shells coating the AuNP, there was a significant decrease in the absorbance maximum of the aggregated particles, from ~578 to ~536 nm, as the shell thicknesses increased from ~1.7 to ~4.3 nm, because of increased distance between adjacent gold cores. These studies provide guidance for the development of colorimetric assays using silica-coated AuNP.
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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".