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Record W4220858566 · doi:10.1021/acsami.2c00058

Strain-Induced Magnetoelectric Coupling in Fe<sub>3</sub>O<sub>4</sub>/BaTiO<sub>3</sub> Nanopillar Composites

2022· article· en· W4220858566 on OpenAlexafffund
Guohua Dong, Tian Wang, Haixia Liu, Yijun Zhang, Yanan Zhao, Zhongqiang Hu, Wei Ren, Zuo‐Guang Ye, Keqing Shi, Ziyao Zhou, Ming Liu, Jingye Pan

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCentral University Basic Research Fund of ChinaState Administration of Foreign Experts AffairsChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsNanopillarMaterials scienceSubstrate (aquarium)SpintronicsComposite materialCoupling (piping)HeterojunctionOptoelectronicsFerroelectricityFerromagnetismNanotechnologyCondensed matter physicsNanostructureDielectric

Abstract

fetched live from OpenAlex

Magnetoelectric coupling properties are limited to the substrate clamping effect in traditional ferroelectric/ferromagnetic heterostructures. Here, Fe 3 O 4 /BaTiO 3 nanopillar composites are successfully constructed. The well-ordered BaTiO 3 nanopillar arrays are prepared through template-assisted pulsed laser deposition. The Fe 3 O 4 layer is coated on BaTiO 3 nanopillar arrays by atomic layer deposition. The nanopillar arrays and heterostructure are confirmed by scanning electron microscopy and transmission electron microscopy. A large thermally driven magnetoelectric coupling coefficient of 395 Oe °C –1 near the phase transition of BaTiO 3 (orthorhombic to rhombohedral) is obtained, indicating a strong strain-induced magnetoelectric coupling effect. The enhanced magnetoelectric coupling effect originated from the reduced substrate clamping effect and increased the interface area in nanopillar structures. This work opens a door toward cutting-edge potential applications in spintronic 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 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 score0.002

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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