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Record W2889391733 · doi:10.1063/1.5020160

Strain effects on the magnetic order of epitaxial FeRh thin films

2018· article· en· W2889391733 on OpenAlexaff
Hardeep Kumar, D.R. Cornejo, Sérgio L. Morelhão, S. Kycia, I. M. Montellano, N. Álvarez, G. Alejandro, A. Butera

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Guelph
FundersConsejo Nacional de Investigaciones Científicas y TécnicasCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAgencia Nacional de Promoción Científica y TecnológicaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceCondensed matter physicsFerromagnetic resonanceFerromagnetismMagnetic anisotropyMagnetizationAntiferromagnetismThin filmEpitaxySubstrate (aquarium)AnisotropyMagnetic shape-memory alloyMagnetic fieldComposite materialNanotechnologyOpticsLayer (electronics)Physics

Abstract

fetched live from OpenAlex

In this work, we report the experimental results obtained on a set of ∼90 nm thick FeRh epitaxial films deposited on MgO (001), MgO (111), and Al2O3 (0001) single crystal substrates. The magnetic characterization was achieved by measuring magnetization curves and ferromagnetic resonance as a function of temperature and orientation of the films with respect to the applied magnetic field. We discuss our results by comparing the characteristics of the antiferromagnetic-ferromagnetic transition among FeRh films of the same thickness but exposed to different post-growth annealings and deposited on substrates of different crystalline orientations. We have found that there is a correlation between the strain present in the films and their magnetic behavior, observing that a change in the in-plane stress from compressive to tensile tends to shift the magnetic transition by more than 60 K. The interplay between magnetic and elastic properties was further analyzed by ferromagnetic resonance, and we have found that the magnetoelastic component of the anisotropy varies from out-of-plane to in-plane, depending on the substrate. These findings could be of great importance if a precise tuning of the magnetic transition temperature or the magnetic anisotropy is needed for a specific application.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.213
Teacher spread0.204 · 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.

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

Citations29
Published2018
Admission routes1
Has abstractyes

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