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A New Advanced Analytical Model for Bi-Layer Circular CMUT-Based Gas Sensors

2019· article· en· W3000210463 on OpenAlexaff
Haleh Nazemi, Arezoo Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersMultiphysicsCapacitive sensingUltrasonic sensorSensitivity (control systems)Materials scienceAcousticsTransducerFinite element methodBandwidth (computing)PiezoelectricityElectronic engineeringOptoelectronicsComputer scienceEngineeringElectrical engineeringPhysicsTelecommunicationsStructural engineering

Abstract

fetched live from OpenAlex

Capacitive Micromachined Ultrasonic Transducers (CMUTs) have been introduced as an alternative candidate to piezoelectric transducers for ultrasonic imaging applications due to their better acoustic matching and higher bandwidth. In this paper and in an unconventional approach, a CMUT configuration is used as a highly-sensitive gas sensor that operates based on the measured frequency shifts in response to the mass of absorbed target analytes. In this work, a new complex bi-layer CMUT gas sensor model is developed that comprises the effect of sensing material properties as well as the sensor structural materials and geometrical design parameters to optimize and enhance the sensor sensitivity based on the application. The proposed analytical model is further utilized to design a high-sensitivity CMUT sensor for low concentration detection of gas molecules. The analytical model analysis and finite element method simulations are also conducted and compared. The results show a high achieved sensitivity of 364 Hz/zg for this sensor. In addition, the results indicate less than 2% variation between the developed analytical bi-layer model and the FEA multiphysics simulations <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

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 score0.714
Threshold uncertainty score0.682

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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