Spectroscopic and Physical Characterization of Functionalized Au Nanoparticles: A Multiweek Experimental Project
Bibliographic record
Abstract
A term project was introduced in teaching advanced spectroscopy and notions of nanotechnology to chemistry students at the graduate level (M.Sc. and Ph.D.). This project could also be suited for an honor’s thesis at the undergraduate level. Students were assigned a unique combination of nanoparticle synthesis (13 nm Au nanospheres, ∼100 nm nanoraspberries or ∼50 nm nanostars) and fluorescent/Raman-active ligand (HS-PEG-FITC, rhodamine 6G, 4-mercaptobenzoic acid, 4-nitrobenzenethiol, and phenanthroline). Characterization with transmission electron microscopy allowed the students to confirm the shape and size distribution of nanoparticles. The ligands immobilized on the surface of the nanoparticles were extensively characterized using a suite of optical techniques. UV–vis was used to observe the plasmonic bands of particles of varying shapes, and fluorescent spectroscopy was used to construct fluorescent pathways with the aid of Jablonski diagrams. The vibrational bands of the ligands were identified using IR and Raman spectroscopy. Performing surface-enhanced Raman spectroscopy (SERS) on the nanoparticle at different excitation wavelength (488, 633, and 785 nm) was used to understand the influence of the surface plasmon on SERS and fluorescence spectroscopies. Students were required to write a full scientific paper for the report of the term project.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".