(Invited) The Role of Pyridine Derivatives in the Formation of Anisotropic Gold Nanoparticles
Bibliographic record
Abstract
It is appealing to develop and understand new approaches to generate anisotropic metal nanoparticles (NPs) owing to their application in optical-based sensing platforms. Empirically, the formation of anisotropic NPs is typically back-rationalized by speculating that shape-directing ions or molecules preferentially adsorb on different crystallographic facets of the growing particle. An alternative strategy is described herein whereby electrochemical measurements of pyridine-derivative adsorption on different low index single crystal surfaces conclusively demonstrate preferentially adsorption on Au(100} surfaces. This serves as a starting basis for developing rational approaches to generate homogeneous and heterogeneous anisotropic NPs through chemical and electrochemical reduction of AuIII precursors. The pyridine derivatives are shown to play a critical role in the formation of Au nanopods and nanodaggers. Electrochemical evidence is provided of a two-step reduction of tetrachloroaurate involving a pyridine-stabilized AuI species which plays a key role in producing anisotropic structures. Both electrodeposited and suspended NPs have surface plasmon resonances that extend well into the near IR (λmax ≈ 1000-1350 nm). Near-IR Raman sensing applications are demonstrated using FT-Raman with 1064 nm excitation. Electrodeposited nanodaggers provide SERS enhancement factors greater than 106 for monolayers of 4-aminothiophenol (4-ATP).
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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) | 0.007 | 0.003 |
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".