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Record W4396542541 · doi:10.1109/jsen.2024.3393636

Highly Compact and Sensitive Dielectric Sensors

2024· article· en· W4396542541 on OpenAlexaff
Ali M. Almuhlafi, Omar M. Ramahi

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Waterloo
FundersKing Saud University
KeywordsDielectricOptoelectronicsMaterials scienceElectrical engineeringElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

This work proposes a novel and simple microwave technique for controlling the coupling factor between complementary split-ring resonators (CSRRs) and microstrip transmission lines (TLs), which can be applied to the design of highly sensitive dielectric sensors. By varying the areas of the rectangular CSRRs while maintaining a fixed perimeter, the side lengths along TLs are varied, causing the coupling factor to be controlled. As a result, the physical sensing areas are miniaturized, which is important for various applications, leading to an enhancement in sensitivity. In addition, we demonstrate that the critical coupling between the external circuit (e.g., TLs) and CSRRs can be easily achieved if required by certain applications. Our simulations and experimental measurements demonstrate that our approach can relatively reduce the physical sensing areas by up to 69.4%. These results can help engineers to design compact sensors. Compared with a recently published work, the enhanced sensitivity for detecting the presence of dielectric materials is 63.16%. Our results provide a roadmap for satisfying different design constraints and specifications and demonstrate the potential for this technique to be adopted in a variety of dielectric sensing and filter applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2024
Admission routes1
Has abstractyes

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