Light Emitting Diode Irradiation Can Control the Morphology and Optical Properties of Silver Nanoparticles
Bibliographic record
Abstract
A facile method for the preparation of silver nanoparticles (AgNPs) of various sizes and morphologies, including dodecahedra, nanorods, and nanoplates, has been discovered. By choosing the desired optical properties (absorption maximum) and irradiating spherical AgNP seeds with a selected light emitting diode, we achieve control over the size, morphology, and optical properties. The properties of AgNPs are intrinsically dependent on the size and shape of the particles, which can be readily controlled with the strategies reported here. Literature methods for the synthesis of anisotropic AgNPs require complex solutions containing seed nanoparticles with specific twinning defects, and a variety of specific stabilizing ligands direct the growth of the seeds but limit the versatility of the particles. In general, these thermal routes to anisotropic AgNPs give particles with high polydispersity, limiting their applications in single molecule spectroscopy and surface plasmon resonance spectroscopy. We describe a single photochemical method for preparation of AgNPs with predictable and controllable size and morphology that are produced from a single source of photochemically grown AgNP seeds stabilized only by sodium citrate. We also describe a common mechanism for the formation of the various types of AgNPs.
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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) | 0.001 | 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".