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Record W2104650003 · doi:10.1109/tuffc.2006.1678196

Fabrication of PZT sol gel composite ultrasonic transducers using batch fabrication micromolding

2006· article· en· W2104650003 on OpenAlexaff
Guofeng Pang, M. Sayer, G.R. Lockwood, Morgan R. Watt

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2006
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsResearch & Development CorporationQueen's University
Fundersnot available
KeywordsLIGAMaterials scienceFabricationResistLithographyUltrasonic sensorPhotolithographyTransducerComposite numberEpoxyCapacitive micromachined ultrasonic transducersPhotoresistSoft lithographyComposite materialOptoelectronicsPiezoelectricityLayer (electronics)Acoustics

Abstract

fetched live from OpenAlex

A new micromolding technique for fabricating high-frequency (>20 MHz) ultrasound transducers has been developed. The technique combines sol gel processing with an epoxy-based, photo-resist Su-8 micromold to form miniature PZT structures. An advantage of this technique as compared to more traditional lithographic galvanforming and abforming (LIGA) processing is that the intermediate step of producing a nickel-plated mold is avoided. Instead, the PZT is formed directly using a photo-resist. The resulting structures can be fabricated with aspect ratios up to 3:1 and thicknesses up to 50 micro. We have successfully fabricated 50-micro-thick linear array elements with 23-micro-wide elements separated by 15 kerfs. A 50-micro thick, 2.5-mm diameter, five-element annular array structure with 20-micro kerfs also has been fabricated. The micromolded PZT composite has a density of 5.7-5.8 micro 0.4 g/cm3 and a thickness coupling coefficient as high as 0.32.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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