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Traveling Sound Wave with Transverse Particle Velocity in a Metawaveguide by Using a Phase-Reversible Metasurface

2020· article· en· W3103973858 on OpenAlexaff
Xiaobing Cai, Zhandong Huang, Jun Yang

Bibliographic record

VenuePhysical Review Applied · 2020
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsWestern University
Fundersnot available
KeywordsTransverse planePhase velocityPhase (matter)AcousticsSound (geography)Particle velocityPhysicsTransverse waveParticle (ecology)OpticsWave propagationGeologyEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

In ideal fluids, acoustic waves are known as longitudinal sound waves, the particle velocity of which is parallel to the wave traveling direction. The emergence of metasurfaces offers extensive flexibility in acoustic wave manipulations. However, achieving a sound wave with transversely polarized particle velocity by means of metasurfaces is not yet achievable. Here, we demonstrate that a sound wave with polarized particle velocity perpendicular to the traveling direction can exist in an acoustic metawaveguide. The metawaveguide is made of a pair of spaced acoustic metasurfaces, each comprising alternatingly arranged membranes that resonate phase reversibly. Bearing transversely polarized particle velocity, the sound wave shows lateral dependence, frequency selectivity, and insensitivity to longitudinal irregularity, all distinct from that in a traditional waveguide. More interestingly, the transverse particle velocity is axis symmetric, and thus, acoustophoresis traps are formed in the center of the metawaveguide. The advent of a sound wave with transverse particle velocity may open another avenue for more sophisticated acoustic applications, such as resistance-free insulation, acoustic ``circuits,'' and wave-matter interaction related bioacoustics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.310
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2020
Admission routes1
Has abstractyes

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