Mesoporous Ag nanocubes synthesized via selectively oxidative etching at room temperature for surface-enhanced Raman spectroscopy
Bibliographic record
Abstract
Silver nanocubes enriched with 100 facets have been extensively used for surface-enhanced Raman scattering. Herein, we report a new water-phase synthesis method for well-defined Ag nanocubes with tunable sizes via a two-step procedure at room temperature. First, irregularly shaped Ag nanoparticles (INPs) were prepared by reducing silver ammonia solution using ethylal. Second, the agglomerated INPs were selectively etched with HNO 3 and Cl − to yield 100 facet-rich mesoporous Ag nanocubes. The mechanism of Ag-nanocube formation and growth was investigated in detail by elucidating the involved chemical reactions and physical changes at each step during the synthesis. The addition of Cl − anions was responsible for facilitating Ag nanoparticle growth by removing surface-adsorbed AgCl + species, thereby eliminating inter-particle repulsive forces. This agglomeration was found crucial for the subsequent selective oxidation of Ag nanoparticles because the protective agent used, polyvinylpyrrolidone (PVP), was the most effective one for adsorption on the surfaces of Ag nanoparticles of size greater than approximately 50 nm. Importantly, mesopores were found inside the Ag nanocubes; this can be attributed to the unavoidable imperfect packing during the agglomeration of INPs. The newly prepared Ag nanocubes were further used to enhance the Raman signal of rhodamine 6G, which is capable of reducing the detection limitation to 10 −16 mol·L −1 .
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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) | 0.001 | 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".