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Record W4213096512 · doi:10.1364/oe.450734

Broadband ultrasound sensing based on fused dual-core chalcogenide-PMMA microfibers

2022· article· en· W4213096512 on OpenAlexafffund

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofiberUltrasonic sensorTransducerUltrasoundBroadbandTaperingCore (optical fiber)Transmission (telecommunications)

Abstract

fetched live from OpenAlex

High-frequency ultrasound sensors are essential for high-resolution medical ultrasonic imaging and industrial ultrasonic non-destructive monitoring. In this paper, we propose highly sensitive broadband ultrasound sensors based on fused dual-core chalcogenide-polymethyl methacrylate (As 2 Se 3 -PMMA) microfibers. We demonstrate that ultrasound response is determined by the differential slope of transmission spectra in the dual-core microfiber, which is verified by detecting the acoustic response in various microfibers of different tapering parameters. A broadband ultrasound frequency range with a high signal-to-noise ratio (SNR) is achieved in the fused dual-core microfiber (DCM) with a sub-micron core diameter and a close core separation due to the large spectral slope at the quadrature points of the transmission spectrum. In addition, we experimentally demonstrate the sensing of ultrasound waves propagating with and without an aluminum plate in the DCM sensor. An ultrasound sensor with a broadband frequency range from 20 kHz to 80 MHz and an average SNR of 31 dB is achieved in a compact fused dual-core As 2 Se 3 -PMMA microfiber when it is directly placed on a piezoelectric transducer (PZT).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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.

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

Citations12
Published2022
Admission routes2
Has abstractyes

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