Non-invasive, non-contact conductivity measurement using radiofrequency probe loading
Bibliographic record
Abstract
Abstract Accurate measurement of electrical conductivity in water-based samples is essential for applications ranging from brine characterization in petroleum systems to biological sensing. Direct-contact methods are prone to electrode degradation, while indirect-contact methods often suffer from undefined current paths. This study introduces a non-invasive, non-contact technique for conductivity measurement in water-based samples using radiofrequency (RF) probe loading, leveraging inductive losses induced by eddy currents in conductive media. Changes in the quality factor of the RF probe are analyzed to establish a direct proportionality between inductive losses and sample conductivity. This relationship is validated across solenoidal, loop-gap resonator, and surface coil designs with cylindrical and planar homogeneous samples. Theoretical equations, derived from classical electromagnetism and the principle of reciprocity, closely align with experimental results, revealing the critical role of probe geometry and resonant frequency in measurement sensitivity. The findings demonstrate robust performance across diverse ionic solutions including samples with flow, achieving cost-effective measurement with minimal hardware. This method will allow differentiation between samples with differing conductivity, even when absolute conductivity values cannot be determined. This makes it especially suited for analyzing complex, heterogeneous materials like foods or biological samples, where localized average conductivity measurement in specific regions of the sample, may be used as a classification tool. The RF probe loading approach offers a versatile alternative to conventional conductivity measurement methods, with potential for real-time, non-invasive monitoring in dynamic systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".