Promote Localized Surface Plasmonic Sensor Performance via Spin-Coating Graphene Flakes over Au Nano-Disk Array
Bibliographic record
Abstract
Although localized surface plasmonic resonance (LSPR) sensors have advantages over regular surface plasmonic resonance (SPR) sensors, such as in sensor setup, excitation method, and cost, they suffer from low performance when compared to SPR sensors, which thus limits their commercialization. Among different methods applied to promote LSPR sensor performance, metal-two-dimensional (2D) hybrid nanostructure has been shown to be an efficient improvement. However, metal-2D hybrid nanostructures may come in a complex or a simple scheme and the latter is preferred to avoid challenges in fabrication work and to be applicable in mass production. In this work, a new and simple gold-graphene hybrid scheme is proposed and its plasmonic sensing performance is numerically evaluated using the finite different time domain (FDTD) method. The proposed sensor can be fabricated by growing a Au nano-disk (ND) array on a quartz substrate and then spin-coating graphene flakes of different sizes and shapes randomly on top of and between the Au NDs. Very high sensitivity value is achieved with 2262 nm/RIU at a 0.01 refractive index change. The obtained sensitivity value is very competitive in the field of LSPR sensors using metal-2D hybrid nanostructure. This proposed sensor can be utilized in different biosensing applications such as immunosensors, sensing DNA hybridization, and early disease detection, as discussed at the end of this article.
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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".