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Record W3094050215 · doi:10.1063/5.0022569

Natural granular pile as electromagnetic ground cloak

2020· article· en· W3094050215 on OpenAlexaff
Xiaobing Cai, Hui Liu, Jun Yang

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsCloakingCloakMetamaterialTransformation opticsElectromagnetic radiationMetamaterial cloakingElectromagneticsPhysicsPermittivityOpticsOptoelectronicsDielectricEngineering physicsMetamaterial absorber

Abstract

fetched live from OpenAlex

Rendering an object invisible through a cloaking device is an ongoing dream of human beings and material scientists. Recently, intensive theories and experiments have predicted and demonstrated that such a cloaking device can be realized based on transformation optics and metamaterials, in fields of electromagnetics, optics acoustics, or even heat transfer. Metamaterials enable precise control over the propagation of waves due to their delicate micro-structures and spatially tailorable properties. However, a simple and natural way to achieve cloaking without a delicate micro-structure remains unattainable. Here, we report that an electromagnetic quasi-cloaking device can be readily achieved by a granular pile, formed by the falling-off and cumulation of particles on the object to be cloaked. We show that natural particle size segregation during the formation of the granular pile imparts the pile with a spatially variable filling ratio and resultant gradient-distributed permittivity, which enables the realization of the electromagnetic cloaking device. This work may open the possibility that a complicated electromagnetic device may be alternately achieved from manipulation of particle movement or arrangement.

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 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 score0.018
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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