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Record W3132794641 · doi:10.3390/s21041459

An Investigation of Silica Aerogel to Reduce Acoustic Crosstalk in CMUT Arrays

2021· article· en· W3132794641 on OpenAlexafffund
Varshitha Yashvanth, Sazzadur Chowdhury

Bibliographic record

VenueSensors · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitive micromachined ultrasonic transducersMaterials scienceAerogelUltrasonic sensorCapacitive sensingCrosstalkNanoporousAcousticsTransducerOptoelectronicsElectronic engineeringNanotechnologyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents a novel technique to reduce acoustic crosstalk in capacitive micromachined ultrasonic transducer (CMUT) arrays. The technique involves fabricating a thin layer of diisocyanate enhanced silica aerogel on the top surface of a CMUT array. The silica aerogel layer introduces a highly nanoporous permeable layer to reduce the intensity of the Scholte wave at the CMUT-fluid interface. 3D finite element analysis (FEA) simulation in COMSOL shows that the developed technique can provide a 31.5% improvement in crosstalk reduction for the first neighboring element in a 7.5 MHz CMUT array. The average improvement of crosstalk level over the -6 dB fractional bandwidth was 22.1%, which is approximately 5 dB lower than that without an aerogel layer. The results are in excellent agreement with published experimental results to validate the efficacy of the new technique.

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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.251
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

Citations14
Published2021
Admission routes2
Has abstractyes

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