Multifaceted prismatic silver nanoparticles: synthesis by chloride-directed selective growth from thiolate-protected clusters and SERS properties
Bibliographic record
Abstract
We describe the synthetic preparation of well-defined symmetric multifaceted prismatic silver nanoparticles with chemically controlled faceting advantageous for strong and tunable surface-enhanced Raman scattering, SERS. These silver nanoparticles, that have been termed nanoflowers, AgNFls for their characteristic morphologies, have been prepared by a one-pot aqueous reaction under ambient conditions. AgNFl faceting is synthetically controlled by selective nanoparticle growth driven by chloride ions. Selective chloride binding to the surface of growing AgNFls results in nanoparticle enlargement predominantly at the points of their highest energy. These growth points are located at the tips of prismatic polygons in precursor prismatic morphologies that have been produced from thiolate-protected silver clusters whose coalescence is triggered with a strong base. For the practical aspects of AgNFl synthesis, concentrations of thiol and a strong base were found to be the key variables reliably controlling the extent of AgNFl faceting, as well as the kinetics of AgNFl formation and their stability. The selective growth of AgNFls progresses slower compared to that of non-faceted prisms: fewer nuclei can form leading to larger AgNFls with the diameter ranging from 130 to 2250 nm and asperity sizes on the order of 20 to 100 nm. Self-assembly of AgNFls yields columnar stacking. AgNFls were demonstrated to function as a promising substrate for surface-enhanced Raman scattering. SERS measurements were performed for a series of AgNFls with variable faceting, where the enhancement factors of 4.6 × 10(8) and 425 have been achieved for dry solid films and aqueous dispersions of non-aggregated AgNFls with single-particle enhancement, respectively. These SERS results are promising, especially in combination with that AgNFl nanoscale asperities can be conveniently tailored synthetically. Overall, AgNFls offer valuable opportunities for a system with synthetically variable nanoscale asperities.
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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".