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Record W2748684366 · doi:10.1021/acsami.7b06876

Ferroelectric Phase Transition Induced a Large FMR Tuning in Self-Assembled BaTiO<sub>3</sub>:Y<sub>3</sub>Fe<sub>5</sub>O<sub>12</sub> Multiferroic Composites

2017· article· en· W2748684366 on OpenAlexafffund
Guohua Dong, Ziyao Zhou, Xu Xue, Yijun Zhang, Bin Peng, Mengmeng Guan, Shishun Zhao, Zhongqiang Hu, Wei Ren, Zuo‐Guang Ye, Ming Liu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaState Administration of Foreign Experts AffairsMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceFerroelectricityMultiferroicsYttrium iron garnetMagnetostrictionFerromagnetic resonanceCondensed matter physicsEpitaxyPhase transitionFerromagnetismPhase (matter)OptoelectronicsComposite materialDielectricMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

Yttrium iron garnet (YIG) is of great importance in RF/microwave devices for its low loss, low intrinsic damping, and high permeability. Nevertheless, tuning of YIG-based multiferroics is still a challenge due to its near-zero magnetostriction and the difficulty of building epitaxial interface between ferromagnetic garnet and ferroelectric perovskite phases. In this work, the vertically aligned heterostructure of YIG:BTO/STO(001) with local epitaxial interface between BTO and YIG is well-constructed, where the single crystal BTO pillars are embedded in YIG matrix. A large magnetoelectric coupling effect that drives YIG's FMR shift up to 512 and 333 Oe (1-2 order greater than those of all state-of-the-art progresses) is obtained through BTO ferroelectric phase changes induced by temperature variation at 295 and 193 K, correspondingly. This record high magnetoelectric tunability of YIG paves a way toward thermal/electrical tunable YIG 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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

Citations24
Published2017
Admission routes2
Has abstractyes

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