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Record W2768893530 · doi:10.1063/1.4986120

Water based fluidic radio frequency metamaterials

2017· article· en· W2768893530 on OpenAlexaff
Xiaobing Cai, Shaolin Zhao, Mingjun Hu, Junfeng Xiao, Naibo Zhang, Jun Yang

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMetamaterialCloakingFluidicsNegative refractionMaterials scienceRefraction3D printingOptoelectronicsNanotechnologyOpticsPhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Electromagnetic metamaterials offer great flexibility for wave manipulation and enable exceptional functionality design, ranging from negative refraction, anomalous reflection, super-resolution imaging, transformation optics to cloaking, etc. However, demonstration of metamaterials with unprecedented functionalities is still challenging and costly due to the structural complexity or special material properties. Here, we demonstrate for the first time the versatile fluidic radio frequency metamaterials with negative refraction using a water-embedded and metal-coated 3D architecture. Effective medium analysis confirms that metallic frames create an evanescent environment while simultaneously water cylinders produce negative permeability under Mie resonance. The water-metal coupled 3D architectures and the accessory devices for measurement are fabricated by 3D printing with post electroless deposition. Our study also reveals the great potential of fluidic metamaterials and versatility of the 3D printing process in rapid prototyping of customized metamaterials.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.272
Teacher spread0.240 · 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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