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A Dual-Frequency Capacitive Micromachined Ultrasonic Transducer (CMUT) for Vapor Detection

2020· article· en· W3081934377 on OpenAlexaff
Zhou Zheng, Jasmine Chan, John T. W. Yeow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersMaterials scienceCapacitive sensingUltrasonic sensorTransducerOptoelectronicsElectrical impedanceWaferGrapheneOxideResonance (particle physics)AcousticsNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

This study reports a novel single-chip dual-frequency (10 MHz/14 MHz) CMUT sensor that exhibits promising capabilities of vapor detection and discrimination. The CMUT is fabricated by the nitride-to-oxide wafer bonding process. Graphene oxide nanosheets are used to functionalize the device and make it sensitive to water vapors. Characterization on the electrical impedance reveals that the two resonances of the device can be easily identified with a single frequency sweep. The sensing performance of the device is analyzed by monitoring its resonance shifts as the relative humidity changes. High sensitivity and fast response/recovery have been achieved. More importantly, the sensor can generate two independent frequency responses due to its multi-resonance feature, showing great potential for discrimination of multiple chemical vapors.

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 score0.506
Threshold uncertainty score0.719

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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