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Record W4389508907 · doi:10.1002/apxr.202300128

Reconfigurable and Phase‐Engineered Acoustic Metasurfaces for Broadband Wavefront Manipulation

2023· article· en· W4389508907 on OpenAlexaff
Kexin Zeng, Zhendong Li, Zichao Guo, Zhonggang Wang

Bibliographic record

VenueAdvanced Physics Research · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Hunan ProvinceNational Natural Science Foundation of China
KeywordsWavefrontBroadbandAcousticsRobustness (evolution)AttenuationRangingComputer sciencePhase responsePhase (matter)Materials scienceOpticsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract A novel type of phase‐engineered acoustic metasurfaces with reconfigurable properties is reported, enabling the flexible broadband manipulation of reflected wavefronts. The participants of the metasurface are elements conceived to possess even‐distributed reflected phases covering 2π span with linearity and small acoustic energy loss. The reconfigurable property of the metasurface is implemented by rearranging the fixed meta‐elements based on the phase profile, which is related to the characteristic of a specific wavefront shape. The metasurface's capability is successfully demonstrated to achieve acoustic focusing and bending within the frequency range of 2300–2800 Hz, showcasing its feasibility and adaptability. To enhance its practical applications, porous materials are incorporated, leveraging the robustness of phase differences among the meta‐elements to achieve high acoustic energy cancellation. Effective sound attenuation occurs within the frequency range of 1300–3100 Hz, even under wide‐angle incidences ranging from −80° to 80°. The work paves the way for further research on reconfigurable acoustic metasurfaces in broad frequency regions and exerts favorable implications for generally applicable structures applied in multi‐fields including biomedical acoustics, noise control, and so on.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.103
GPT teacher head0.379
Teacher spread0.276 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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