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Record W4308451006 · doi:10.1109/tuffc.2022.3215791

Recent Advances in Piezoelectric Materials for Electromechanical Transducer Applications

2022· article· en· W4308451006 on OpenAlexaff
Fei Li, Andrew J. Bell, Dragan Damjanović, Wook Jo, Zuo‐Guang Ye, Shujun Zhang

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPiezoelectricityTransducerPMUTCapacitorAcousticsFerroelectricityActuatorEnergy harvestingUltrasonic motorElectrical engineeringMaterials scienceUltrasonic sensorPiezoelectric sensorUnderwaterEnergy (signal processing)Mechanical engineeringEngineering physicsEngineeringVoltagePhysicsGeology

Abstract

fetched live from OpenAlex

Ferroelectricity has made a huge impact on science and technology since Joseph Valasek (then a Ph.D. student at the University of Minnesota) discovered it in 1920, a little longer than 100 years ago. Whereas Dr. Valasek’s original research was motivated by the need to develop seismic sensors, at present ferroelectric materials have been extensively studied for applications in high-energy capacitors, energy harvesting systems, night vision sensors, and electrocaloric solid cooling, and, of particular significance, the ferroelectrics are the material-of-choice for numerous electromechanical devices, including underwater acoustic transducers, medical diagnostic and therapeutic transducers, piezoelectric actuators, and ultrasonic motors, to name a few. The progress over the past 100 years has been enormous and it is ongoing: for example, the piezoelectric coefficient d of ferroelectrics has increased from a few pico-Coulomb per Newton to several thousand pico-Coulomb per Newton, which benefits all piezoelectric sensing and actuation 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.217
Teacher spread0.210 · 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.

Study designOther design
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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

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