FDTD Analysis of Hotspot-Enabling Hybrid Nanohole-Nanoparticle Structures for SERS Detection
Bibliographic record
Abstract
Metallic nanoparticles (MNPs) and metallic nanostructures are both commonly used, independently, as SERS substrates due to their enhanced plasmonic activity. In this work, we introduce and investigate a hybrid nanostructure with strong SERS activity that benefits from the collective plasmonic response of the combination of MNPs and flow-through nanohole arrays (NHAs). The electric field distribution and electromagnetic enhancement factor of hybrid structures composed of silver NPs on both silver and gold NHAs are investigated via finite-difference time-domain (FDTD) analyses. This computational approach is used to find optimal spatial configurations of the nanoparticle positions relative to the nanoapertures and investigate the difference between Ag-NP-on-Ag-NHAs and Ag-NP-on-Au-NHAs hybrid structures. A maximum GSERS value of 6.8 × 109 is achieved with the all-silver structure when the NP is located 0.5 nm away from the rim of the NHA, while the maximum of 4.7 × 1010 is obtained when the nanoparticle is in full contact with the NHA for the gold-silver hybrid structure. These results demonstrate that the hybrid nanostructures enable hotspot formation with strong SERS activity and plasmonic enhancement compatible with SERS-based sensing applications.
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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".