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Record W2572625758 · doi:10.1109/tmtt.2016.2645147

Permittivity and Conductivity Measured Using a Novel Toroidal Split-Ring Resonator

2017· article· en· W2572625758 on OpenAlexaff

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsResonatorPermittivityQ factorHelical resonatorDielectricDielectric resonator antennaResonance (particle physics)Relative permittivity

Abstract

fetched live from OpenAlex

We describe and demonstrate the use of a novel toroidal split-ring resonator to make accurate and precise measurements of the permittivity of liquids and gases. We first analytically show how the resonance frequency and quality factor of the resonator are related to the complex permittivity of the material filling its gap. We then use the resonator to experimentally determine the permittivity of a number of different materials. First, the compact and high-Q resonator is used to measure both the real and imaginary parts of the complex permittivity of methyl alcohol at 185 MHz. Second, the resonator was placed inside a vacuum-tight Dewar. We measured the resonance frequency with the resonator suspended in vacuum and then immersed in an atmosphere of air. From these data, the dielectric constant of air was accurately determined. Next, the resonator was submerged in liquid nitrogen, and the boiling temperature of the nitrogen bath was manipulated by regulating its vapor pressure. This system allowed for a precise measurement of the dielectric constant of liquid nitrogen over a temperature range of 64 to 77 K. Finally, we monitored the quality factor of the copper resonator as its temperature drifted from 80 K to room temperature. From these data, we extracted the linear temperature dependence of copper's resistivity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.266
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2017
Admission routes1
Has abstractyes

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