Far-field and Near-field Optical Properties Of Strongly Interacting Silver Nanocrystals
Bibliographic record
Abstract
Noble metal nanocrystals are known for their remarkable optical properties that are caused by the result of their ability to support a localized surface plasmon resonance (LSPR).These optical properties can be observed in the far-field, through their optical extinction or in the near-field by a highly enhanced electric-field localised to the surface of the particles.The goal of this work was to study properties of supported silver nanocrystal ensembles as they interact with their substrate, neighbouring nanocubes and the resulting mutual interactions.Demonstrated is the ability to finely tune plasmonic properties of silver nanocube monolayers by controlling the interparticle spacing and the properties of the underlying substrate.Control over these properties is applied to the optimisation of substrates used for surface-enhanced Raman spectroscopy (SERS).Control over the plasmonic properties is achieved by directing the assembly of silver nanocube (AgNC) ensembles.The primary means to do so is by using the Langmuir-Blodgett technique to control nanoparticle surface density.These monolayers are placed on a number of substrates such as glass, silicon thin films, and titanium oxide thin films.Demonstrated in this work is the ability to shift plasmonic modes using interparticle interactions or particle-substrate interactions.The local electric field enhancement in these monolayers is investigated thoroughly by SERS, and demonstrated is the dependence of the enhancement on particle cluster size, charge transfer processes, and the location of the target molecule in the monolayer.By gaining insight into how these interactions affect the local electric field at the surface of the nanoparticles, lessons gained through this work could be applied towards the optimization of surface enhanced Raman spectroscopy.
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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) | 0.002 | 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".