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Dynamic Piezoelectric Behavior of Lithium Niobate at High Temperature

2016· article· en· W2551076727 on OpenAlexaff
Hector de Castilla, Pierre Bélanger, Ricardo J. Zednik

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsMaterials scienceLithium niobateCurie temperaturePiezoelectricityResonant ultrasound spectroscopyDielectric spectroscopyPiezoelectric sensorComposite materialCondensed matter physicsOptoelectronicsFerromagnetismElectrochemistryElectrode

Abstract

fetched live from OpenAlex

High temperature piezoelectric materials have numerous potential applications, including high temperature ultrasound NDT, MEMS, sensors, or actuators. However, conventional piezoelectric materials are unsuitable for operation above 400°C. Lithium niobate (LiNbO 3 ) is a promising candidate because of its very high Curie temperature (approximately 1210°C) and reasonable piezoelectric coefficients. However, the piezoelectric properties are not sufficiently understood, partly due to the difficulties in characterizing this behavior at high temperature. Degradation mechanisms well below the Curie temperature, suspected to include phase transformations, oxygen loss, and excessive ionic conductivity, further deteriorate this property. In order to better understand these physical mechanisms, electrochemical impedance spectroscopy (EIS) is used to characterize monocrystalline LiNbO 3 from room temperature to 500°C, with excitations from 20 Hz to 20 MHz. An equivalent circuit model analysis, including resonant frequencies, is developed to investigate the temperature dependence of the piezoelectric behavior, as well as the mechanical elasticity and damping. Numerical values extracted from this analysis allows for numerical simulations to model device behavior.

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 score0.003
Threshold uncertainty score0.434

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 science0.0010.000
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 head0.004
GPT teacher head0.204
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 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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