Controlled Growth of Gold Nanoparticles Preorganized in Langmuir–Blodgett Monolayers
Bibliographic record
Abstract
A method is described for the in situ growth of substrate-supported organized gold nanoparticles. Upon exposure to an aqueous solution of a gold salt and a mild reducing agent, the particle size can be significantly increased without any loss of superstructure organization. Furthermore, no secondary nucleation is observed. The surface-supported regrowth procedure can be combined with the Langmuir-Blodgett technique to produce a rich library of plasmonic nanoparticle assemblies. Controlled particle regrowth plays a crucial role in this assembly method because only relatively small metallic nanoparticles can be directly dispersed in polymeric Langmuir-Blodgett films. The versatility of the method is demonstrated through the fabrication of several specific nanoparticle structures, including contiguous plasmonic rings, core-satellite structures, and necklace assemblies. Plasmon extinction spectra are presented for the various nanoparticle superstructures and illustrate the importance of controlling both particle size and assembly architecture in achieving targeted optical properties. The reported approach constitutes a viable bottom-up assembly route for the fabrication of surface-supported nanoparticle superstructures for plasmonic 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".