High-Resolution Chipless Tag RF Sensor
Bibliographic record
Abstract
In this article, a new coupled structure based on microwave planar resonators is proposed to demonstrate the capability of ultrahigh quality factor performance using a chipless passive resonator. The platform is based on two passive split-ring resonators (SRRs), one as a reader, and the other one as the tag. The reader resonator is designed to operate at 2.6-GHz resonance frequency and is coupled to an active feedback loop with a microwave amplifier to compensate for the resonator's loss and increase the loaded quality factor of the response. The bandwidth of the feedback amplifier is modified such that the amplifier's gain is higher at the resonance frequency of the tag than that of the reader by adding a capacitor between emitter and collector of the bipolar junction transistor (BJT) amplifier. The tag is designed at 1.6 GHz and is located at a 2.5-mm vertical distance from the reader. As a result, the overall quality factor of about 75 000 is achieved for the tag performing the sensing. The presented technique provides a great practical solution for highly sensitive, noninvasive, and real-time sensing applications. The proposed sensing tag is integrated with a microfluidic chip to highlight its capability for small volume sensing and lab-on-a-chip applications. The sensitivity potential of the design is verified by detecting the concentration of acetone in deionized water. The average sensitivity of the presented sensor is more than 35 kHz/(1% of acetone concentration variation), which is offering extremely high sensitivity of the structure considering the very small volume of the exposed material under the test and the distance between the sensor and the sample.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".