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Record W4401069829 · doi:10.1109/lsens.2024.3434672

Expanding Limit of Detection and Increasing Operating Resonant Frequency via Larger Anchor Widths for Capacitive Micromachined Resonator-Based Mass Sensors

2024· article· en· W4401069829 on OpenAlexaff
Annalise Gignac, Akib Shamsuddin, Haleh Nazemi, Muhammad Umair Nathani, Arezoo Emadi

Bibliographic record

VenueIEEE Sensors Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsResonatorCapacitive sensingLimit (mathematics)Detection limitOptoelectronicsMaterials scienceAcousticsElectrical engineeringPhysicsEngineeringChemistryMathematics

Abstract

fetched live from OpenAlex

An innovative technique is explored, aiming to expand the limit of detection (LOD) and increase the operating resonant frequency of the capacitive micromachined ultrasonic transducer (CMUT) working as a mass sensor through modifying anchor widths. The LOD is associated with the concentration range of target compounds and, therefore, their correspondent mass that can be measured using a CMUT mass sensor. The poly multiuser MEMS process (PolyMUMPs) sacrificial technique is used to fabricate three CMUT sensors with a range of anchor widths while keeping all other sensor design parameters and material properties constant. Experimental analysis is conducted to evaluate the influence of anchor width to resonant frequency and frequency shift at different biasing conditions. The experimental results demonstrate that a larger anchor width can increase CMUT operating resonant frequency and pull-in voltage, allowing for the expanded LOD. These results also agree with analytical expressions presented. In addition, it is shown that through employing a designed anchor width, resonant frequencies can be varied to fulfill specific sensor requirements with minimal fabrication adjustments. This study contributes to the evolving knowledge of the CMUT as a mass sensor, enabling more methods for variability of these devices.

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: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.963

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 head0.011
GPT teacher head0.247
Teacher spread0.237 · 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
Published2024
Admission routes1
Has abstractyes

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