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Record W1999737265 · doi:10.1115/imece2010-40508

Piezoelectric Strain Measurement for Low Power Microsensor Applications

2010· article· en· W1999737265 on OpenAlexaff
Alborz Amini, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPiezoelectricityMaterials scienceTransducerElectrical impedanceFinite element methodAcousticsPiezoelectric coefficientMechanical impedanceStress (linguistics)Piezoelectric sensorTransverse planePower (physics)Electronic engineeringElectrical engineeringEngineeringComposite materialPhysicsStructural engineering

Abstract

fetched live from OpenAlex

Many transducers use mechanical strain measurements as their sensing method. We are proposing a mechanical strain measurement technique based on a change in the electrical impedance and frequency response of a piezoelectric film in a certain frequency. This piezoelectric transducer can be fabricated on top of substrate for measuring surface stress. This technique is provided with analytical and numerical models. Coupled field finite element simulations in COMSOL and Coventorwareare employed to confirm the measurable alteration in electrical impedance of the film due to applied normal and transverse stress. Piezoelectric material impedance measurement can be a precise and low power method to sense minuscule strains in micromechanical structures. To the best of our knowledge, this is the first demonstration of this technique at micro-scales.

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)0.001
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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