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Record W3204600492 · doi:10.1364/osac.426779

Bias-sensitive transparent single-element ultrasound transducers using hot-pressed PMN-PT

2021· article· en· W3204600492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOSA Continuum · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsDalhousie UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsTransducerMaterials scienceLithium niobateElectrostrictionUltrasonic sensorDC biasBiasingOptoelectronicsPhotoacoustic effectVoltageBandwidth (computing)AcousticsPiezoelectricityOpticsPhotoacoustic imaging in biomedicineElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Here we introduce electrostrictive hot-pressed lead magnesium niobate (PMN) with low lead titanate (PT) doping as a candidate transparent transducer material. We fabricate transparent high-frequency single-element transducers and characterize their optical, electrical, and acoustic properties. PMN-PT may offer sensitivity advantages over other transducer materials such as lithium niobate owing to its high electromechanical efficiency and bias-voltage sensitivity. The transparency of the fabricated transducer was measured ∼67% at 532 nm wavelength with a maximum electromechanical coefficient of ∼0.68 with a DC bias level of 100 V. The photoacoustic impulse response showed a center frequency of ∼27.6 MHz with a −6 dB bandwidth of ∼61% at a DC bias level of 40 V. Results demonstrate that the new transparent transducers hold promise for future optical-ultrasonic and photoacoustic imaging applications.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.145
Threshold uncertainty score1.000

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 head0.040
GPT teacher head0.235
Teacher spread0.195 · 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