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Characterization of piezoelectric materials for simultaneous strain and temperature sensing for ultra-low frequency applications

2015· article· en· W900576130 on OpenAlexaff
Mohammad N. Islam, Rudolf Seethaler, M. Shahria Alam

Bibliographic record

VenueSmart Materials and Structures · 2015
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPiezoelectricityMaterials scienceCapacitancePiezoelectric sensorCharacterization (materials science)Piezoelectric coefficientPiezoelectric accelerometerStrain (injury)AcousticsElectronic engineeringOptoelectronicsComposite materialNanotechnologyEngineeringElectrodePhysics

Abstract

fetched live from OpenAlex

Piezoelectric materials are used extensively in a number of sensing applications ranging from aerospace industries to medical diagnostics. Piezoelectric materials generate charge when they are subjected to strain. However, since measuring charge is difficult at low frequencies, traditional piezoelectric sensors are limited to dynamic applications. In this research an alternative technique is proposed to determine static strain that relies upon the measurement of piezoelectric capacitance and resistance using piezoelectric sensors. To demonstrate the validity of this approach, the capacitance and resistance of a piezoelectric patch sensor was characterized for a wide range of strain and temperature. The study shows that the piezoelectric capacitance is sensitive to both strain and temperature while the resistance is mostly dependent on the temperature variation. The findings can be implemented to obtain thermally compensated static strain from piezoelectric sensors, which does not require an additional temperature sensor.

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 score0.022
Threshold uncertainty score0.478

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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