Synthesis of Silver Nanoprisms with Variable Size and Investigation of Their Optical Properties: A First-Year Undergraduate Experiment Exploring Plasmonic Nanoparticles
Bibliographic record
Abstract
A robust and reasonably simple experiment is described that introduces students to the visualization of nanoscale properties and is intended for a first-year laboratory. Silver nanoprisms (NPs) that display different colors due to variation of their plasmonic absorption with respect to size are prepared. Control over the size of the silver nanoprisms is achieved using a novel approach, where bromide is added to the reaction as a size-determining agent, and silver ions are reduced by borohydride in the presence of citrate and peroxide as stabilizing and shape-directing agents, respectively. In a typical experiment, four dispersions of silver nanoprisms with different sizes are produced that are colored from blue (largest) to yellow (smallest). The colors attainable in between are violet, purple, red, and orange. The synthesis of these colored silver NPs is described for the first time as an undergraduate experiment. Once synthesized, the nanoprisms are characterized using UV−vis spectroscopy, and a Beer’s law experiment can be performed. Furthermore, concepts of redox chemistry and visible spectroscopy can be reinforced in this experiment, and it can be further adapted for upper-level laboratories focused on the investigation of nanoparticle properties by more advanced techniques.
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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) | 0.000 | 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 | 0.001 | 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".