MétaCan
Menu
Back to cohort
Record W2136430062 · doi:10.1109/iembs.1995.575277

Sol-gel PZT thick films for ultrasonic imaging

2002· article· en· W2136430062 on OpenAlexaff
M. Lukács, M. Sayer, D.A. Knapik, R. Candela, F. Stuart Foster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsSunnybrook Health Science CentreQueen's University
Fundersnot available
KeywordsMaterials scienceLead zirconate titanateTransducerUltrasonic sensorFabricationUltrasoundResolution (logic)Image resolutionAcoustic microscopyThin filmOpticsAcousticsMicroscopyOptoelectronicsFerroelectricityNanotechnologyDielectricComputer science

Abstract

fetched live from OpenAlex

Ultrasound transducers for B-mode medical imaging are typically operated between 1 and 10 MHz. Research has been directed towards developing transducers that are sensitive in the 10-100 MHz range because a number of clinical problems require higher resolution than can be achieved at the lower frequencies. Using a modified sol-gel technique developed at Queen's University, lead zirconate titanate (PZT) thin films of 45/spl plusmn/5 /spl mu/m in thickness have been successfully coated on aluminum foil. The technique allows the fabrication of thin films of arbitrary thickness in the range of 1 to 60 /spl mu/m. This PZT provides an alternative transducer material in the 10-100 MHz range and may also produce sensitive transducers operating above 100 MHz. This will increase the available resolution of ultrasound backscatter microscopy and narrows the gap between lower resolution clinical systems and high resolution transmission scanning acoustic microscopy. The evaluation of the electrical and mechanical properties of these PZT films and the ability to pattern them are described.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.016
GPT teacher head0.253
Teacher spread0.236 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicUltrasound Imaging and ElastographyFrench-language works237,207