Mode-Coupling Generation Using ITO Nanodisk Arrays with Au Substrate Enabling Narrow-Band Biosensing
Bibliographic record
Abstract
Plasmonic metal nanostructures have promising applications in biosensing due to their ability to facilitate light-matter interaction. However, the damping of noble metal leads to a wide full width at half maximum (FWHM) spectrum which restricts sensing capabilities. Herein, we present a novel non-full-metal nanostructure sensor, namely indium tin oxide (ITO)-Au nanodisk arrays consisting of periodic arrays of ITO nanodisk arrays and a continuous gold substrate. A narrow-band spectral feature under normal incidence emerges in the visible region, corresponding to the mode-coupling of surface plasmon modes, which are excited by lattice resonance at metal interfaces with magnetic resonance mode. The FWHM of our proposed nanostructure is barely 14 nm, which is one fifth of that of full-metal nanodisk arrays, and effectively improves the sensing performance. Furthermore, the thickness variation of nanodisks hardly affects the sensing performance of this ITO-based nanostructure, ensuring excellent tolerance during preparation. We fabricate the sensor ship using template transfer and vacuum deposition techniques to achieve large-area and low-cost nanostructure preparation. The sensing performance is used to detect immunoglobulin G (IgG) protein molecules, promoting the widespread application of plasmonic nanostructures in label-free biomedical studies and point-of-care diagnostics. The introduction of dielectric materials effectively reduces FWHM, but sacrifices sensitivity. Therefore, utilizing structural configurations or introducing other materials to generate mode-coupling and hybridization is an effective way to provide local field enhancement and effective regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".