Insight into the Role of Ag in the Seed-Mediated Growth of Gold Nanorods: Implications for Biomedical Applications
Bibliographic record
Abstract
The synthesis of gold nanorods (AuNRs) with a specific length/diameter aspect ratio is crucial for their use in imaging, sensing, drug delivery, and biological applications. However, the most commonly used silver-aided seed-mediated synthesis method still suffers from poor outcome predictability and hence, in an overall sense, reproducibility. To address this information gap, the mechanism of the seed-mediated synthesis has been investigated, particularly with regard to the possible existence of limiting reagents or intermediates in the reaction. The key silver intermediate which controls the AuNR aspect ratio has thus been identified as a CTA–Ag–Br complex. The AuNR growth solution preparation process has been systematically investigated and the solubility of the CTA–Ag–Br complex is established to be the limiting agent in the preparation and growth of the resulting AuNR. The sequence of reagent addition is shown to also be a determinant in the evolution of a resulting gold nanorod. The importance of the CTA–Ag–Br complex in nanorod syntheses is supported by the observation of gold NP formation when a reductant (ascorbic acid) is added before the CTA–Ag–Br complex has formed. This result informs the understanding of the role of the silver ion in the AuNR synthesis and provides a much-needed entry to the synthesis of AuNRs with custom aspect ratios, including those with much sought after large aspect ratios. This result will benefit research involving AuNRs, especially that in biomedical applications.
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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 | 0.001 |
| 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".