A low-cost photonic band gap (PBG) microstrip line resonator for dielectric characterization of liquids
Bibliographic record
Abstract
Abstract In this paper, hybrid integration of planar microstrip line and Photonic Band Gap (PBG) structure is proposed for the dielectric characterization of liquids. To implement the PBG structure of the microstrip line, a microfluidic channel with periodic form is introduced into the substrate and filled with different liquids. Based on this configuration, the operation principle of the sensor is based on a frequency shift due to the variation in the center of the bandgap, which in turn changes with the variation of the permittivity of LUT filled in the microfluidic channel. The proposed sensor exploits the behavior of the bandgap as a reflector to construct a resonant structure sensitive to the variation in LUT permittivity. The dimensions of the planar structure are optimized to achieve high precision and discrimination capability. The different empirical expressions describing the complex permittivity with the measured parameters were carried out. To validate the proposed concept, the sensor prototype is designed, fabricated, and tested. The frequency shift related to a change of 3.2 in LUT permittivity corresponds to 180 MHz around 6 GHz. The resonant-mode sensor spans a permittivity range from 1 to 80 with a precision better than 7.2%. The proposed sensor is simple in design and low cost, which may be applied in different applications at the industrial.
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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 | 0.001 |
| 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".