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A Robust Microwave Sensor Featuring a Perfect Metasurface Absorber

2024· article· W7131085475 on OpenAlexaff
Nazli Kazemi, Mohammad Abdolrazzaghi, Petr Musilek, Elham Baladi

Bibliographic record

Venuenot available
Typearticle
Language
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsPolytechnique MontréalUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsMicrowaveCapacitorDielectricSensitivity (control systems)Polarization (electrochemistry)Absorption (acoustics)Microwave applicationsInductor

Abstract

fetched live from OpenAlex

A novel microwave sensor featuring a perfect meta-surface absorber, enhanced by LC-loaded transmission lines, has been developed, representing a significant advancement in noninvasive sensing. This sensor integrates strategically placed lumped inductors and series capacitors within its unit cell, leading to improved resolution and lowered absorption frequency. Consequently, it achieves over 98% absorption efficiency across a range of polarization angles, enhancing sensitivity and accuracy in detecting minimal material quantities. Its design ensures stable performance under varying incident angles and polarizations, making it versatile for numerous noninvasive sensing applications. Simulations have shown its ability to detect linear frequency shifts over a wide dielectric range (1 to 80), highlighting its precision and potential to redefine standards in metasurface-based microwave sensing. This development underscores the sensor’s importance in advancing microwave sensing technologies and its broad applicability.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.272
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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