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Record W2585926647 · doi:10.1002/adfm.201605598

Modulation of Spin Dynamics via Voltage Control of Spin‐Lattice Coupling in Multiferroics

2017· article· en· W2585926647 on OpenAlexaff
Mingmin Zhu, Ziyao Zhou, Bin Peng, Shishun Zhao, Yijun Zhang, Gang Niu, Wei Ren, Zuo‐Guang Ye, Yaohua Liu, Ming Liu

Bibliographic record

VenueAdvanced Functional Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsSimon Fraser University
FundersNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsMagnonicsCondensed matter physicsMultiferroicsFerromagnetic resonanceSpin waveMaterials sciencePhysicsFerromagnetismSpin Hall effectSpin polarizationElectronMagnetic fieldOptoelectronicsFerroelectricityQuantum mechanicsMagnetization

Abstract

fetched live from OpenAlex

Motivated by the most recent progresses in both magnonics (spin dynamics) and multiferroics fields, this work aims at magnonics manipulation by the magnetoelectric coupling effect. Here, voltage control of magnonics, particularly the surface spin waves, is achieved in La 0.7 Sr 0.3 MnO 3 /0.7Pb(Mg 1/3 Nb 2/3 )O 3 ‐0.3PbTiO 3 multiferroic heterostructures. With the electron spin resonance method, a large 135 Oe shift of surface spin wave resonance (≈7 times greater than conventional voltage‐induced ferromagnetic resonance shift of 20 Oe) is determined. A model of the spin‐lattice coupling effect, i.e., varying exchange stiffness due to voltage‐induced anisotropic lattice changes, has been established to explain experiment results with good agreement. Additionally, an “on” and “off” spin wave state switch near the critical angle upon applying a voltage is created. The modulation of spin dynamics by spin‐lattice coupling effect provides a platform for realizing energy‐efficient, tunable magnonics 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)0.0000.000

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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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

Citations55
Published2017
Admission routes1
Has abstractyes

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