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Record W4226392740 · doi:10.1364/oe.455796

Flexible transparent CMUT arrays for photoacoustic tomography

2022· article· en· W4226392740 on OpenAlexafffund
Mahyar Ghavami, Afshin Kashani Ilkhechi, Roger J. Zemp

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsCapacitive micromachined ultrasonic transducersMaterials scienceTomographyOpticsTransducerCenter frequencyRadius of curvatureBandwidth (computing)Capacitive sensingUltrasonic sensorWaferCurvatureOptoelectronicsAcousticsPiezoelectricityTelecommunications

Abstract

fetched live from OpenAlex

This paper reports the fabrication and characterization of the first flexible transparent capacitive micromachined ultrasound transducer (CMUT) array for through-illumination photoacoustic tomography. Fabricated based on an adhesive wafer bonding technique and a PDMS backfill approach, the array has a maximum transparency of 67% in visible light range and can be bent to a radius of curvature of less than 5 mm without the structural layers being damaged. With a center frequency of 3.5 MHz, 80% fractional bandwidth, and noise equivalent pressure (NEP) of 62 mPa/ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msqrt> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">H</mml:mi> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> , the array was successfully used in limited-view photoacoustic tomography of a 100 µ m wire target, demonstrating lateral and axial resolutions of 293 µ m and 382 µ m, respectively, with 46 dB signal-to-noise ratio. Additionally, deep tissue photoacoustic tomography was also demonstrated on a blood tube within a chicken tissue using the fabricated CMUT arrays.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.864

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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

Citations34
Published2022
Admission routes2
Has abstractyes

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