Assessing the Location of Surface Plasmons Over Nanotriangle and Nanohole Arrays of Different Size and Periodicity
Bibliographic record
Abstract
The increasing popularity of surface plasmon resonance (SPR) and surface enhanced Raman scattering (SERS) sensor design based on nanotriangle or nanohole arrays, and the possibility to manufacture substrates at the transition between these plasmonic substrates, makes them ideal candidates for the establishment of structure-property relationships. This work features near diffraction-limited Raman images and FDTD simulations of nanotriangle and nanohole arrays substrates, which clearly demonstrate that the localization of the hot spot on these SERS substrates is significantly influenced by the ratio of diameter/periodicity (D/P). The experimental and simulation data reveal that the hot spots are located around nanotriangles (D/P = 1), characteristic of localized SPR. Decreasing the D/P ratio to 0.75-0.7 led to the creation of nanohole arrays, which promoted the excitation of a propagating surface plasmon (SP) delocalized over the metal network. The optimal SERS intensity was consistently achieved at this transition from nanotriangles to nanoholes, for every periodicity (650 nm to 1.5 μm) and excitation wavelength (633 and 785 nm) investigated, despite the presence or absence of a plasmonic band near the laser excitation. Further decreasing the D/P ratio led to excitation of a localized SP located around the rim of nanohole arrays for D/P of 0.5-0.6, in agreement with previous reports. In addition, this manuscript provides the first evidence that the hot spots are positioned inside the hole for D/P of 0.4, with the center being the region of highest electric field and Raman intensity. The compelling experimental evidence and FDTD simulations offer an overall understanding of the plasmonic properties of nanohole arrays as SERS and SPR sensors, which is of significant value in advancing the diversity of applications from such surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".