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Record W4408503270 · doi:10.1063/5.0254952

Inhomogeneous magnetoelectric structures

2025· article· en· W4408503270 on OpenAlexfundno aff
М. И. Бичурин, Oleg Sokolov, С. В. Іванов, E. Е. Ivasheva, Ivan Markov, Yaojin Wang

Bibliographic record

VenueJournal of Applied Physics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsnot available
FundersCanadian Wildlife Health Cooperative
KeywordsMaterials scienceCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

Many studies have been devoted to the magnetoelectric (ME) effect in connection with its possible use in the creation of new promising electronic devices. Special attention is paid to the analysis of the ME structure, which mainly determines the properties of a new ME device. At the same time, in practice, experimental studies of inhomogeneous ME structures often prevail and the theoretical calculation of which, as a rule, is quite complex. The authors consider the calculation of inhomogeneous ME structures in the longitudinal and bending modes in this paper. It is of practical interest to take into account the inhomogeneities associated with the location of the electrodes and the different lengths of the piezoelectric and magnetostrictive components of the ME structure. The results obtained showed that the excess of the magnetic component length determines the value of the converse ME coefficient, and in the case of using a symmetric structure to create low-frequency ME antennas, the optimal value of the excess parameter is 1.4–2.5. In the opposite case, reducing the length of the magnetic component by 25% in order to connect the electrodes to the asymmetric structure leads to a significant decrease in the ME voltage coefficient to 50%. At the same time, the use of variable-size electrodes for an asymmetric structure in the bending mode indicates the possibility of a significant increase in the ME voltage coefficient. A comparison of theoretical and experimental results is carried out.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

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