Tunable 3D Plasmonic Cavity Nanosensors for Surface-Enhanced Raman Spectroscopy with Sub-femtomolar Limit of Detection
Bibliographic record
Abstract
Metallic nanohole arrays (NHAs) with a high hole density have emerged with potential applications for surface-enhanced Raman spectroscopy (SERS) including the detection of analytes at ultralow concentrations. However, these NHA structures generally yield weak localized surface plasmon resonance (LSPR), which is a prerequisite for SERS measurements. In this work, a compact three-dimensional (3D) tunable plasmonic cavity with extraordinary optical transmission properties serves as a molecular sensor with sub-femtomolar detection. The 3D nanosensor consists of a gold film containing an NHA with an underlying cavity and a gold nanocone array at the bottom of the cavity. These nanosensors provide remarkable surface plasmon polariton (SPP) and LSPR coupling, resulting in a significantly improved detection performance. The plasmonic tunability is evaluated both experimentally and theoretically. A SERS limit of detection of 10 –16 M for 4-nitrothiophenol is obtained along with distribution mapping of the molecule on the 3D plasmonic nanosensor. This results in an improved SERS enhancement factor of 10 7 obtained from a femtoliter plasmonic cavity volume. The tunability of these sensors can give rise to a potential opportunity for use in optical trapping while providing SERS sensing of a molecule of interest.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".