EOT or Kretschmann configuration? Comparative study of the plasmonic modes in gold nanohole arrays
Bibliographic record
Abstract
The debate is still ongoing on the optimal mode of interrogation for surface plasmon resonance (SPR) sensors. Comparative studies previously demonstrated that nanoparticles exhibiting a localized SPR (LSPR) have superior sensitivity to molecular adsorption processes while thin Au film-based propagating SPR is more sensitive to bulk refractive index. In this paper, it is demonstrated that nanohole arrays (1000 nm periodicity, 600 nm diameter and 125 nm depth), which support both LSPR and propagating SPR modes, exhibited superior sensitivity to bulk refractive index and improved detection limits for IgG sensing by using the Kretschmann configuration. The greater sensitivity to IgG detection in the Kretschmann configuration was obtained despite the shorter penetration depth of nanohole arrays excited in the enhanced optical transmission (EOT) configuration. The decay length of the electromagnetic field in EOT mode was estimated to be approximately 140 nm using a layer-by-layer deposition technique of polyelectrolytes (PAH and PSS) and was confirmed with 3D FDTD simulations, which was lengthen by almost a factor of two in the Kretschmann configuration. Spectroscopic data and field depth were correlated with RCWA and FDTD simulations, which were in good agreement with the experimental results. Considering these analytical parameters, it is advantageous to develop sensors based on nanohole arrays in the Kretschmann configuration of SPR.
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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 | 0.001 |
| 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 | 0.001 |
| 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".