MétaCan
Menu
Back to cohort
Record W4310585417 · doi:10.1109/ius54386.2022.9957153

Evaluation of Piezoelectric Ceramics for use in Miniature Histotripsy Transducers

2022· article· en· W4310585417 on OpenAlexaff
Matthew Mallay, Justin Greige, Thomas Landry, Colton Campbell, Jeffrey Woodacre, Mahmoud A. A. Ibrahim, Jeremy Brown

Bibliographic record

Venue2022 IEEE International Ultrasonics Symposium (IUS) · 2022
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceTransducerParyleneCeramicPiezoelectricityVoltageComposite materialCavitationElectrical impedanceAcousticsEpoxyElectrical engineeringPolymer

Abstract

fetched live from OpenAlex

Five piezoelectric ceramics were evaluated for use in miniature broadband histotripsy transducers. The materials tested were PZT-5A, PZT-5H (CTS 3203HD), Pz39, Pz54 and PMN-38. 5 MHz, 1–3 dice and fill piezo/epoxy composites were fabricated for all except for Pz39, which is a low acoustic impedance porous ceramic. Four air-backed transducers for each material were built by bonding the composite to an 8 mm machined aluminum lens with a 7 mm focus and depositing a Parylene-C matching layer. The performance of each material was evaluated by measuring the transducers' transmitting sensitivity, cavitation threshold voltage in water and surface pressure versus drive voltage, and then testing them to failure. Measured sensitivities were compared to FEM model results, and HITU Simulator V2.0 was used to non-linearly extrapolate the results. Transducers were tested using 8-cycle, 1 kHz PRF pulses. As expected, the measured sensitivity for transducers of each material increased with increasing dielectric constant, while cavitation threshold voltages in water decreased. All Pz39 transducers failed when driven at or above 264 Vpp. All PMN38 transducers failed due to high instantaneous current. The most common failure mode was Parylene delamination.

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)0.002
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 score0.706
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.292
Teacher spread0.264 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venue2022 IEEE International Ultrasonics Symposium (IUS)Same topicUltrasound Imaging and ElastographyFrench-language works237,207