Structured Split-Ring Resonator for Sensing Applications: Dielectric-Material Characterization and Label-Free Detection of Biomolecules
Bibliographic record
Abstract
The classic split-ring resonator (SRR) is structured in this paper to optimize the frequency-shifting sensitivity of SRR-based RF/microwave sensors. The SRR is designed on the top layer of a substrate and another mirrored SRR is duplicated in the ground plane of the substrate. The two SRRs are electrically connected to each other to realize closed-loop structures inside the substrate, which results in the engineered structured split-ring resonator (SSRR). It is shown that the frequency-variation sensitivity in this approach of RF/microwave sensor is significantly increased by using the proposed SSRR by more than 200% in relation to conventional counterparts. The experimental results confirm a sensitivity enhancement by a ratio of 2.2:1 with regard to a sensor with among the highest sensitivities ever reported for high-permittivity lossy-material characterization. Furthermore, the sensor is experimentally examined in a biomedical scenario to monitor antibody, demonstrating a sensitivity enhancement by a ratio of 5:1 compared to a recent SRR-based sensor counterpart.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".