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Record W2213212367 · doi:10.1115/imece2014-39381

Facile and Effective Phononic Structures for Ultrasound Focusing Application

2014· article· en· W2213212367 on OpenAlexaff
Qiuquan Guo, Xiaobing Cai, Jun Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMetamaterialAcoustic metamaterialsMaterials scienceAcousticsUltrasoundTherapeutic ultrasoundAcoustic waveUltrasound energyWavelengthPenetration depthOpticsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Ultrasound therapy is a highly popular non-invasive method to treat diseases. It is desired to increase the resolution of ultrasound therapy, providing more efficient therapeutic effect. In this study, a simple but effective phononic structure was designed to achieve higher resolution close to wavelength resolution of ultrasound focusing. The objective of this study is to design a unique phononic structure to focus acoustic wave into human body more deeply and precisely. It is known that overheating by high frequency ultrasound will cause a series of side effects including bone burns, soft tissue burns, swelling and bleeding problems. In addition, due to the limited propagation of recovered evanescent wave, the penetration depth of acoustic energy being focused by the acoustic metamaterials is often hindered. In this study, we will design a multilayer acoustic metamaterial which shows sub-wavelength focusing ability at relatively low frequencies. More importantly, our design will also extend the ability of penetration depth by manipulating the focusing length through optimization of the phononic structure.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.272

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 studies0.0000.000
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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