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Record W1999815950 · doi:10.1088/0953-8984/26/5/055012

Magnetoelectric Fe<sub>2</sub>TeO<sub>6</sub>thin films

2014· article· en· W1999815950 on OpenAlexafffund
Junlei Wang, Juan A. Santana, Ning Wu, Chithra Karunakaran, Jian Wang, P. A. Dowben, Christian Binek

Bibliographic record

VenueJournal of Physics Condensed Matter · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDefense Advanced Research Projects AgencyMaterials Research Science and Engineering Center, University of NebraskaSemiconductor Research CorporationUniversity of SaskatchewanCanadian Light SourceNational Science Foundation
KeywordsMagnetizationCondensed matter physicsAntiferromagnetismMaterials scienceMagnetic circular dichroismPulsed laser depositionMagnetic momentThin filmMultiferroicsMagnetic fieldPhysicsFerroelectricityNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

We demonstrate that Fe2TeO6 is a magnetoelectric antiferromagnet with voltage-controllable boundary magnetization. This provides experimental evidence of the theoretical prediction that boundary magnetization is a universal property of magnetoelectric antiferromagnets including boundary magnetization at a surface orthogonal to the polar direction. Highly (110) textured Fe2TeO6 thin films were grown by pulsed laser deposition, terminating in Te-O at the (110) surface due to a surface reconstruction. Magnetic dc susceptibility measurements of both Fe2TeO6 powder and thin film samples confirm antiferromagnetic long-range order. Finally, measurements of x-ray magnetic circular dichroism combined with photoemission electron microscopy (XMCD-PEEM) provide a lower bound to the spin and angular magnetic moment of the surface Fe ions.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations26
Published2014
Admission routes2
Has abstractyes

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