Design and Analysis of Highly Sensitive LSPR-Based Metal–Insulator–Metal Nano-Discs as a Biosensor for Fast Detection of SARS-CoV-2
Bibliographic record
Abstract
For over 2 years, the coronavirus has been the most urgent challenge to humanity, and the development of rapid and accurate detection methods is crucial to control these viruses. Here, a 3D FDTD simulation of Au/SiO2/Au metal–insulator–metal (MIM) nanostructures as a biosensor was performed. The strong coupling between the two plasmonic interfaces in the Au/SiO2/Au cavity helped us to obtain relatively higher sensitivity. The attachment of SARS-CoV-2 changed the refractive index, which was used to detect SARS-CoV-2. Due to the higher overlapping of plasmonic mode with the environment of nano-discs, a higher sensitivity of 312.8 nm/RIU was obtained. The peak wavelength of the proposed structure shifted by approximately 47 nm when the surrounding medium refractive index changed from 1.35 (no binding) to 1.5 (full binding). Consequently, the SPR peak intensity variation can be used as another sensing mechanism to detect SARS-CoV-2. Finally, the previously reported refractive index changes for various concentrations of the SARS-CoV-2 S-glycoprotein solution were used to evaluate the performance of the designed biosensor.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".