Two-Step Synthesis of Large Gold–Silver Alloy Nanoparticles via the Combination of Seeded Growth and Citrate Co-reduction
Bibliographic record
Abstract
We report a novel two-step method for the synthesis of uniform quasi-spherical and monodisperse gold–silver alloy nanoparticles with diameters of approximately 80 nm. In step one, gold–silver alloy seeds (G0) with diameters of approximately 30 nm are synthesized by citrate co-reduction of gold acetate and silver nitrate. In step two, the seeds grow into large alloy nanoparticles with diameters of approximately 80 nm by the combination of seeded growth and citrate co-reduction of gold and silver precursors. Our method makes use of gold acetate (rather than chloroauric acid) as a gold precursor to avoid the formation of silver chloride precipitates during synthesis. To our knowledge, we are the first to prepare gold–silver alloy nanoparticles with diameters of above 70 nm using gold acetate as a gold precursor. The alloy seeds and seed-grown nanoparticles show good control of size and composition, which enables tuning of the plasmonic band of these gold–silver alloy nanoparticles. Gold and silver are distributed uniformly within the cores of all Au-Ag alloy nanoparticles, while the surface is enriched in silver. This silver-enriched surface layer is thicker and more prominent for 25% Au alloy nanoparticles than for 50% and 75% Au alloy nanoparticles.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".