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Record W4303449162 · doi:10.1115/1.4055917

Acoustic Wave Manipulation by Phase Conjugate Metasurface

2022· article· en· W4303449162 on OpenAlexaff
Xiaobing Cai, Zhandong Huang, Chun-Guang Wang, Peipei Jia, Jun Yang, Liwen Zhang

Bibliographic record

VenueJournal of vibration and acoustics · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsWestern University
Fundersnot available
KeywordsWavefrontOpticsBeam steeringPhase (matter)PlanarReflection (computer programming)PhysicsRefractionAcousticsElectrical impedanceLens (geology)Phase conjugationBeam (structure)Computer science

Abstract

fetched live from OpenAlex

Abstract Metasurfaces are advantageous in wavefront manipulation owing to their compact and flat nature. Particularly, ultrathin and completely smooth metasurfaces with giant phase delay and perfect impedance match are critically required for practical applications. Here, we propose an ultrathin and holeless metasurface composed of simply a pair of membranes. This metasurface supports duo unity transmissions with completely conjugate phase shifts occurring at two extremely close frequencies. This allows the metasurface to present giant phase delay and endow with high refractive index (n = 18) when the wave penetrates through. Such a property is employed to control the wavefront of acoustic waves to realize planar lens focusing, negative refraction, negative reflection, and directional emission. The proposed design principle of acoustic metasurface provides promising avenues for acoustic wave manipulation and may enable extensive applications in beam steering, acoustic imaging, energy harvesting, and surface waves.

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 science0.0000.001
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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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