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Record W4322743857 · doi:10.3390/s23052677

UWB Frequency-Selective Surface Absorber Based on Graphene Featuring Wide-Angle Stability

2023· article· en· W4322743857 on OpenAlexaff
Zhefei Wang, Jiajun Huang, Dongjiao Sun, Qingsheng Zeng, Mingxin Song, Tayeb A. Denidni

Bibliographic record

VenueSensors · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsBroadbandMaterials scienceResonatorTunable metamaterialsWidebandGrapheneBandwidth (computing)Polarization (electrochemistry)Impedance matchingElectrical impedanceOpticsMetamaterial absorberOblique caseAbsorption (acoustics)AcousticsOptoelectronicsComputer scienceEngineeringPhysicsTelecommunicationsElectrical engineeringMetamaterialComposite materialNanotechnology

Abstract

fetched live from OpenAlex

In this paper, an ultra-wideband and polarization-insensitive frequency-selective surface absorber is presented with oblique incident stable behavior. Different from conventional absorbers, the absorption behavior is much less deteriorated with the increase in the incidence angle. Two hybrid resonators, which are realized by symmetrical graphene patterns, are employed to obtain the desired broadband and polarization-insensitive absorption performance. The optimal impedance-matching behavior is designed at the oblique incidence of electromagnetic waves, and an equivalent circuit model is used to analyze and facilitate the mechanism of the proposed absorber. The results indicate that the absorber can maintain a stable absorption performance with a fractional bandwidth (FWB) of 136.4% up to 40°. With these performances, the proposed UWB absorber could be more competitive in aerospace 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 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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.228
Teacher spread0.210 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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