Role of Au(I) Intermediates in the Electrochemical Formation of Highly Anisotropic Gold Nanostructures with Near-IR SERS Applications
Bibliographic record
Abstract
In the presence of certain stabilizing ligands, such as pyridine derivatives, the reduction of Au(III) ions has been speculated to generate Au(I) intermediates that may play a key role in nanoparticle growth. Herein, the electrochemical behavior of Au(III) in the presence of 4-methoxypyridine, Py, is reported in aqueous electrolytes. Voltammetric analysis reveals that a spontaneously formed Au(III)–Py complex undergoes a two-step reduction process. The first reduction involves the transfer of two electrons and produces a Au(I) species. A more cathodic one-electron transfer results in electrodeposited gold. Sustained generation of the Au(I)–Py intermediate species produced from the first reduction step leads to disproportionation and the formation of aggregated nanoparticle meshes that loosely adhere to the ITO electrode. Conversely, application of more negative potentials leads to the formation of highly anisotropic nanodaggers from the electrodeposition of the Au(I) species. The shape-directing properties of Py adsorbed on the nucleated gold result in preferential ⟨111⟩ growth. The length scale of the deposited dagger-like shapes is dependent on deposition potential and deposited charge, and arms extending several hundred nanometers are reported. Optical characterizations show extinction extending well into the near-infrared region, which is attributed to localized surface plasmonic resonances. Near-IR Raman sensing applications are demonstrated using FT-Raman with 1064 nm excitation. The nanodaggers provide SERS enhancement factors greater than 10 6 for monolayers of 4-aminothiophenol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".