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Record W2313912361 · doi:10.1177/016173460502700104

Design and Fabrication of Ultrafine Piezoelectric Composites

2005· article· en· W2313912361 on OpenAlexaff
Jian‐Hua Yin, M. Lukács, Kasia Harasiewicz, F. Stuart Foster

Bibliographic record

VenueUltrasonic Imaging · 2005
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsWafer dicingMaterials sciencePiezoelectricityFabricationTransducerComposite materialUltrasonic sensorComposite numberFinite element methodElectromechanical coupling coefficientVolume (thermodynamics)AcousticsCoupling (piping)Structural engineeringLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

Making fine scale (< 20 microm) piezoelectric composites for high frequency (> 50 MHz) ultrasound transducers remains challenging. Interdigital phase bonding (IPhB), described in this paper, presents a new technique developed to make piezoelectric composites at the ultrafine scale using a conventional dicing saw. Using the IPhB technique, a composite structure with a pitch that is less than the dicing saw blade thickness can be created. The approach is flexible enough to make composites of different combination of pitch and volume ratio. Using a conventional dicing saw with a 50 microm thick blade, composite with a 25 microm pitch and a volume ratio of 61 percent are fabricated. Such a composite is suitable for fabrication of ultrasonic transducers and arrays with central frequencies of up to 85 MHz. Single element transducers working at central frequencies of 50-60 MHz were made of these composites as a mean to characterize the acoustic performance. Measurement results of the transducers show that the longitudinal electromechanical coupling coefficient is greater than 0.6 and that there are no noticeable lateral resonances in the frequency range of 55-150 MHz. Design criteria for fine scale elements are also discussed based on theoretical results from finite element analysis (FEA).

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: none
Teacher disagreement score0.837
Threshold uncertainty score0.478

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 head0.191
Teacher spread0.186 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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