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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 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)0.001
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.002

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 head0.006
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 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

Citations12
Published2014
Admission routes2
Has abstractyes

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