Improved Synthesis of Gold and Silver Nanoshells
Bibliographic record
Abstract
Metallic nanoshells have been in evidence as multifunctional particles for optical and biomedical applications. Their surface plasmon resonance can be tuned over the electromagnetic spectrum by simply adjusting the shell thickness. Obtaining these particles, however, is a complex and time-consuming process, which involves the preparation and functionalization of silica nanoparticles, synthesis of very small metallic nanoparticles seeds, attachment of these seeds to the silica core, and, finally, growing of the shells in a solution commonly referred as K-gold. Here we present synthetic modifications that allow metallic nanoshells to be obtained in a faster and highly reproducible manner. The main improved steps include a procedure for quick preparation of 2.3 ± 0.5 nm gold particles and a faster approach to synthesize the silica cores. An investigation on the effect of the stirring speed on the shell growth showed that the optimal stirring speeds for gold and silver shells were 190 and 1500 rpm, respectively. In order to demonstrate the performance of the nanoshells fabricated by our method in a typical plasmonic application, a method to immobilize these particles on a glass slide was implemented. The immobilized nanoshells were used as substrates for the surface-enhanced Raman scattering from Nile Blue A.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".