A CSRR-Based Sensor for Full Characterization of Magneto-Dielectric Materials
Bibliographic record
Abstract
In this paper, a novel complementary split-ring resonator (CSRR)-based sensor for full characterization of magneto-dielectric materials is proposed. In general, the operation of microwave resonance-based sensor hinges on the shift in the resonance frequency and the change in the quality factor of the loaded structure. However, both the electric permittivity and the magnetic permeability of the material under test (MUT) have similar effect on the response of the sensor that makes the simultaneous determination of the permittivity and permeability challenging. To remove this difficulty, the main idea behind this paper is to localize the highest intensity of the electric and magnetic fields in two separate zones. By the analysis of the measured resonance frequency and quality factor, the real and imaginary parts of the electric permittivity and the magnetic permeability of the MUT can be determined. Although the characterization of the permittivity and permeability of materials using split-ring resonator and CSRR-based sensors has been widely used, to the best of our knowledge, the full characterization of magneto-dielectric materials using a single sensor has not yet been reported in this paper. As a proof of concept, the sensor was fabricated and used to measure the permittivity and permeability of several materials. Strong agreement between the extracted values and the reference data was achieved.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".