Silver‐Assisted Synthesis of Gold Nanorods: the Relation between Silver Additive and Iodide Impurities
Bibliographic record
Abstract
Abstract Seed‐mediated methods employing cetyltrimethylammonium bromide (CTAB) as a surfactant, and silver salts as additives, are the most common synthetic strategies for high‐yield productions of quality Au nanorods. However, the mechanism of these reactions is not yet fully understood and, importantly, significant lab‐to‐lab reproducibility issues still affect these protocols. In this study, the direct correlation between the hidden content of iodide impurities in CTAB reagents, which can drastically differ from different suppliers or batches, and the optimal concentration of silver required to maximize the nanorods yield is demonstrated. As a result, high‐quality nanorods are obtained at different iodide contents. These results are interpreted based on the different concentrations of CTAB and cetyltrimethylammonium iodide (CTAI) complexes with Ag+ and Au+ metal ions in the growth solution, and their different binding affinity and reduction potential on distinct crystallographic planes. Notably, the exhaustive conversion of CTAI–Au+ to CTAI–Ag+ appears to be the key condition for maximizing the nanorod yield.
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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 | 0.001 |
| 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".