Polyvinylpyrrolidone Molecular Weight Controls Silica Shell Thickness on Au Nanoparticles with Diglycerylsilane as Precursor
Bibliographic record
Abstract
Several strategies have been described for the preparation of silica-encapsulated gold nanoparticles (SiO(2)-AuNP), which typically suffer from an initial interface between gold and silica that is difficult to control, and layer thicknesses that are very sensitive to minor changes in silane concentration and incubation time. The silica shell thicknesses are normally equal to or larger than the gold particles themselves, which is disadvantageous when the particles are to be used for biodiagnostic applications. We present a facile and reproducible method to produce very thin silica shells (3-5 nm) on gold nanoparticles: the process is highly tolerant to changes in reaction conditions. The method utilized polyvinylpyrrolidone (PVP) of specific molecular weights to form the interface between gold and silica. The method further requires a nontraditional silica precursor, diglycerylsilane, which efficiently undergoes sol-gel processing at neutrality. Under these conditions, higher molecular weight PVP leads to thicker silica shells: PVP acts as the locus for silica growth into an interpenetrating organic-inorganic hybrid structure.
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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) | 0.001 | 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) | 0.001 | 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".