A Pixelated Microwave Near-Field Sensor for Precise Characterization of Dielectric Materials
Bibliographic record
Abstract
A highly sensitive microwave near-field sensor based on electrically-small planar resonators is proposed for highly accurate characterization of dielectric materials. The proposed sensor was developed in a robust complete-cycle topology optimization procedure wherein first the sensing area was pixelated. By maximizing the sensitivity as our goal, a binary particle swarm optimization algorithm was applied to determine whether each pixel is metalized or not. The outcome of the optimization is a pixelated pattern of the resonator yielding the maximum possible sensitivity. A curve fitting method was applied to the full-wave simulation results to derive a closed form expression for extracting the dielectric constant of a chemical material from the shift in the resonance frequency of the sensor. As a proof of concept, the sensor was fabricated and used to measure the permittivity of two known liquids (cyclohexane and chloroform) and their mixtures with different volume ratios. The experimentally extracted dielectric constants were in an excellent agreement with the reference data (for pure cyclohexane and chloroform) or those obtained by mixture formulas.
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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) | 0.001 | 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) | 0.001 | 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".