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Record W1601574072 · doi:10.1109/jsen.2015.2429124

Design and Modeling of a Chevron MEMS Strain Sensor With High Linearity and Sensitivity

2015· article· en· W1601574072 on OpenAlexaff
Maziar Moradi, Siva Sivoththaman

Bibliographic record

VenueIEEE Sensors Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSensitivity (control systems)LinearityCapacitanceFinite element methodMaterials scienceMicroelectromechanical systemsElectronic engineeringFabricationAcousticsEngineeringStructural engineeringOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Two new physical designs for sensors that can be used for strain measurement in large structures, such as bridges, wind turbines, or airplanes, are presented. While the proposed sensor designs focus on high sensitivity, they are based on simple operating principle of comb-drive differential variable capacitances and chevron displacement amplification. The chevron beams convert small amount of applied strains to measurable changes in capacitance of comb fingers. The design of the structures enables simple fabrication methods for the realization of the sensors. Two designs are proposed with the first design can also be used as a sensitive resonant strain sensor. Device performances are validated both by analytical solutions and also by finite-element method simulations. The obtained nominal capacitance is 25 fF, with sensitivities of 13 and 2.7 aF per microstrain (με) while demonstrating a maximum strain range of ±1000 and ±1800 με, respectively, for the first and second designs. As a resonant strain sensor, the first design exhibits a sensitivity of ~8.6 Hz/με.

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: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.468

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 head0.040
GPT teacher head0.240
Teacher spread0.200 · 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
Published2015
Admission routes1
Has abstractyes

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