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Marble game with optimal ferroelectric switching

2014· article· en· W2049884408 on OpenAlexafffund
Julian D. Cole, S. J. Ahmed, Laura Curiel, Samuel Pichardo, Oleg Rubel

Bibliographic record

VenueJournal of Physics Condensed Matter · 2014
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsSunnybrook Health Science CentreLakehead UniversityThunder Bay Regional Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development and InnovationNorthern Ontario Heritage Fund Corporation
KeywordsCoercivityFerroelectricityPolarization (electrochemistry)ExcitationMaterials scienceAnisotropySwitching timeCondensed matter physicsHysteresisOpticsPhysicsOptoelectronicsDielectricChemistry

Abstract

fetched live from OpenAlex

A low coercivity and, consequently, low hysteresis loss are desired properties for ferroelectric materials used in high-power and high-frequency actuators. The coercive field required for the onset of ferroelectric switching is shown to develop a strong directional anisotropy due to peculiarities of an energy surface associated with the polarization rotation. It is found that the ferroelectric anisotropy exhibits 'hard' and 'easy' switching axes similar to magnetic materials. The hard axis corresponds to 180° polarization reversal, whereas the easy axis favors 90° switching. Our results suggest that the intrinsic low coercivity and the full polarization reversal may not be achieved at the same time under uniaxial excitation. A rotating electric field excitation is proposed in order to circumvent this limitation and to guide the polarization switching along a curved path.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.006
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.

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

Citations12
Published2014
Admission routes2
Has abstractyes

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