Microwave resonator array with liquid metal selection for narrow band material sensing
Bibliographic record
Abstract
A microwave resonator array is integrated with liquid metal to select an individual resonator response within a resonator array, enabling simple and accurate analysis for dielectric sensing. Galinstan, a liquid metal, acts as a multiplexer by inducing a capacitive load to the nearby resonator, lowering its resonant frequency, and thereby isolating its resonant response from other resonators in the array. The liquid metal could be positioned within a fluidic channel to be above any of the resonators, which tuned the resonant frequency from 3.9 to 3.3 GHz where it can be analyzed individually. The resonators showed a consistent response to liquid metal tuning, with tuning error measured below 30 MHz (5%). The sensor also exhibited stable sensitivity to test materials placed on the selected resonator, with a maximum resonant frequency shift of 300 MHz for a dielectric test material (ε = 10.2) and almost no variation in resonant amplitude. The selected resonant response was only sensitive to materials on the selected resonator, and was unaffected by test materials, even when placed on other resonators. The presented design enabled robust and accurate detection of materials using planar microwave resonators that can be controlled at a user's convenience, specifically for use in systems where multiple parameters or system settings may need to be individually determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".