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Record W2023919494 · doi:10.1121/1.2932723

Three-dimensional phononic crystals made by brazing aluminum beads to form an opal structure

2008· article· en· W2023919494 on OpenAlexaff
J. H. Page, Hefei Hu, Yukihiro Tanaka, Takuro Okada, Shin Tamura

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceSPHERESCrystal (programming language)RodBand gapCubic crystal systemFinite-difference time-domain methodScatteringCoupling (piping)Bragg's lawAluminiumOpticsCondensed matter physicsComposite materialOptoelectronicsPhysicsDiffraction

Abstract

fetched live from OpenAlex

Most phononic crystals studied so far are constructed from periodically arranged objects (e.g., spheres or rods) immersed in a continuous solid or fluid medium. In this presentation, we investigate the properties of a different type of phononic crystal made by weakly brazing 4-mm-diameter aluminum beads in a face-centred cubic array, producing a large-scale opal. In this crystal, wave propagation proceeds through the network of coupled beads rather than through the surrounding medium (air in our case), and the main band gaps that we observe result from coupled resonances of the beads rather than from Bragg scattering. Using ultrasonic techniques, we measure the transmission coefficient and the dispersion relation along the ΓL direction, and show that the lowest-frequency gap occurs between about 500 and 600 kHz - a frequency range that is just below the lowest resonance of an isolated aluminum sphere. We have also measured the group velocity, which we find to be negative in the lowest frequency band gap. Further insight into the properties of these phononic crystals is obtained by performing finite difference time domain (FDTD) calculations, in which the coupling between spheres is taken into account by allowing the spheres to overlap.

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 score0.778
Threshold uncertainty score0.547

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
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 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

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