Dielectric Spectrum Extraction of Liquids Using Noncontact Microwave Split Ring Resonator
Bibliographic record
Abstract
This article presents a novel method for broadband dielectric spectroscopy of liquids utilizing a noncontact microwave split ring resonator (SRR). The proposed approach leverages the multiharmonic property of the microwave resonator to extract the permittivity of the liquid-under-test (LUT) at resonance frequencies. By fitting these extracted values using the Debye model, both the real and imaginary dielectric spectra of the LUT can be determined across a wide frequency range. The proposed method requires a one-time calibration process to ensure high accuracy. An experiment employing five calibration samples and five test samples was performed to validate the effectiveness of the proposed method. The results of the extracted permittivity spectrum using the proposed method are compared with those from a commercial dielectric probe. The proposed method exhibits high accuracy with a root mean square error of 0.59 for$\epsilon'$values at the resonance frequencies. For the extracted Debye model parameters, the proposed method achieves reasonable precision with percentage errors of 1.40% for$\epsilon_s$, 12.68% for$\epsilon_{\infty}$, and 7.86% for$\tau$and is a cost-effective and accurate solution for dielectric spectroscopy. Due to its planar structure, the microwave SRR can be seamlessly integrated into various systems, holding potential for applying this method in microwave sensing, noninvasive biological sensing, oil and gas industries, and other fields requiring dielectric spectroscopy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".