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Record W4212811574 · doi:10.1109/tuffc.2022.3152174

Fabrication and Characterization of a 5 mm × 5 mm Aluminum Lens-Based Histotripsy Transducer

2022· article· en· W4212811574 on OpenAlexafffund
Jeffrey Woodacre, Thomas Landry, Jeremy A. Brown

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2022
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsDalhousie University
FundersAtlantic Canada Opportunities Agency
KeywordsMaterials scienceParyleneAblationTransducerLens (geology)OpticsFabricationBiomedical engineeringAcousticsComposite material

Abstract

fetched live from OpenAlex

Two 5 mm by 5 mm square aluminum lenses with a 6 mm depth of focus were machined and tested for histotripsy with a 40% volume fraction 1-3 PZT-5A composite and a Meggitt Pz-39 porous ceramics lapped to 315 [Formula: see text] as the piezoelectric elements. The devices were air-backed, and an 89 [Formula: see text] layer of Parylene-C was deposited on the lens, matching aluminum to water. Both devices were driven single-ended at 5.8 MHz, their optimal frequency after bonding to the lens, with ten cycles at a PRF of 1 kHz. The composite-based device showed no sign of free-field cavitation in water up to a drive level of 600 V, whereas the Pz39-based device was able to cavitate in water at a drive level of 220 V. In vivo ablation of a rat brain tissue was demonstrated through an opening in the skull and required the drive voltage be increased to 280 V. The ablation was monitored using B-mode imaging with an endoscopic 30 MHz ultrasound phased array and power Doppler overlay. Ablation was maintained for 12 s and, in the power Doppler image, the ablation zone grew steadily over this time to 1.9 mm by 3.4 mm. Immediately after treatment, the ablated area appeared anechoic, slowly filling with specular material.

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 score0.844
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlSame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207