Numerical Investigation of a High-Quality Factor Refractometric Nano-Sensor Comprising All-Dielectric Metamaterial Structures
Bibliographic record
Abstract
This paper proposes an optical sensor based on nanoscale metamaterial structures. The design of the sensor has been explored with respect to biosensing applications through numerical modeling and analysis. The sensor comprises silica substrate and diamond nanostructures, both of which represent dielectrics. The sensing principle is based on the detection of ambient refractive index change. As the analyte properties change, the refractive index changes, as well. The refractive index change has been detected by striking electromagnetic waves onto the structure and noting the spectral response. Ultraviolet waves have been utilized for recording spectral responses and evaluating sensor performance. The sensor displays multiple sharp resonance peaks in the reflected beam. By altering the refractive index of the analyte present around the sensor, the peaks can be seen choosing different wavelengths. The resonance peaks have been investigated to observe electric and magnetic field dipoles in the sensor structure. The spectrum peaks have also been studied to understand fabrication tolerances. The sensor displays a linear response, along with a large Quality (Q) factor. The maximum value of the achieved Quality (Q) factor for the proposed sensor is 1229 while operating across the refractive index range of 1.4–1.45. The claim has been supported by comparison with contemporary works on similar platforms. A range of other sensing parameters have also been calculated and benchmarked. Metamaterial-based optical sensors can provide smaller device sizes, faster response times and label-free detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".