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Record W4230295337 · doi:10.1002/pssb.200405363

Phononic crystals

2004· article· en· W4230295337 on OpenAlexafffund
J. H. Page, Alexey Sukhovich, Suxia Yang, M. L. Cowan, François Van Der Biest, Arnaud Tourin, Mathias Fink, Zhengyou Liu, C. T. Chan, Ping Sheng

Bibliographic record

Venuephysica status solidi (b) · 2004
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of TorontoUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsAcoustic metamaterialsUltrasonic sensorMaterials scienceSlabWavelengthQuantum tunnellingScatteringDispersion (optics)Acoustic waveOpticsAcousticsTransmission coefficientCondensed matter physicsNegative refractionBand gapMetamaterialPhysicsTransmission (telecommunications)OptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract Phononic crystals are periodic composite materials with lattice spacings comparable to the acoustic wavelength. They are of interest not only because of the profound effects of their periodic structure on wave propagation (e.g., the existence of acoustic band gaps), but also because of potential applications (e.g., their possible role in sound filters, transducer design and acoustic mirrors). In this paper, we summarize recent progress using ultrasonic experiments to investigate both two‐ and three‐dimensional phononic crystals. By measuring the ultrasonic wave field transmitted through slab‐shaped samples of different thicknesses, both the dispersion curves and amplitude transmission coefficient can be determined. Because the field is pulsed, the dynamics of the wave fields can also be investigated; this has allowed us to make a systematic study of ultrasonic wave tunneling in phononic crystals. New results on resonant tunneling, focussing and negative refraction phenomena in phononic crystals are also presented. Our data are well explained using Multiple Scattering Theory, giving additional insight into the physical properties and potential applications of these novel materials. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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 score0.006
Threshold uncertainty score0.019

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.244
Teacher spread0.231 · 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

Citations82
Published2004
Admission routes2
Has abstractyes

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