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Record W4392903839 · doi:10.1364/ol.513749

Fabrication of elliptical-silica microfiber for ultrasound detection

2024· article· en· W4392903839 on OpenAlexafffund
Haokun Yang, Gerard Tatel, Yuan Wang, Liang Chen, Xiaoyi Bao

Bibliographic record

VenueOptics Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofiberMaterials scienceOpticsUltrasoundUltrasonic sensorFabricationAcousticsBroadbandBandwidth (computing)PhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Elliptical shape microfiber enables many higher order modes compared with a circular microfiber. The small difference in the optical path length among many modes enabled multi-resonance peaks with high contrast in Mach-Zehnder (MZI) interferometers, which allows a large dynamic range and minimum detection sensitivity for broadband ultrasound sensing. In this paper, we present the design and fabrication of an ultra-compact elliptical-silica microfiber utilizing off-axis flame-drawing for ultrasound detection. The narrow transmission peak showed high contrast for ultrasensitive ultrasound wave detection. With a major-axis diameter of 6.25 µm, the elliptical-silica microfiber sensor exhibits a broadband ultrasound frequency response spanning from 20 kHz to 38.5 MHz. Furthermore, it achieves a signal-to-noise ratio (SNR) of up to 80 dB at 1 MHz, which is the resonance frequency of the microfiber and the linear response under driving voltages of 3-10 V for the PZT ultrasound generator. This low-cost microfiber sensor offers exceptional sensitivity across a broad ultrasonic bandwidth response, making it an ideal choice for nondestructive testing (NDT) and medical imaging applications. Its compact size and immunity to electric and magnetic fields further enhance its utility in various environments.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.584
Threshold uncertainty score0.476

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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