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Chiral bobbers and skyrmions in epitaxial FeGe/Si(111) films

2018· article· en· W2796624917 on OpenAlexaff
Adam Ahmed, James Rowland, Bryan D. Esser, S. R. Dunsiger, David W. McComb, Mohit Randeria, Roland Kawakami

Bibliographic record

VenuePhysical Review Materials · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
FundersDefense Advanced Research Projects AgencyNational Science Foundation
KeywordsSkyrmionMaterials scienceCondensed matter physicsMolecular beam epitaxyEpitaxyPhase (matter)Transmission electron microscopyMetastabilityTexture (cosmology)Thin filmMagnetizationNanotechnologyMagnetic fieldLayer (electronics)PhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

We report experimental and theoretical evidence for the formation of chiral bobbers---an interfacial topological spin texture---in FeGe films grown by molecular beam epitaxy. After establishing the presence of skyrmions in FeGe/Si(111) thin-film samples through Lorentz transmission electron microscopy and the topological Hall effect, we perform magnetization measurements that reveal an inverse relationship between the film thickness and the slope of the susceptibility $(d\ensuremath{\chi}/dH)$. We present evidence for the evolution as a function of film thickness $L$ from a skyrmion phase for $L<{L}_{\mathrm{D}}/2$ to a cone phase with chiral bobbers at the interface for $L>{L}_{\mathrm{D}}/2$, where ${L}_{\mathrm{D}}\ensuremath{\sim}70\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$ is the FeGe pitch length. We show using micromagnetic simulations that chiral bobbers, earlier predicted to be metastable, are in fact the stable ground state in the presence of an additional interfacial Rashba Dzyaloshinskii-Moriya interaction.

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 head0.012
GPT teacher head0.285
Teacher spread0.274 · 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

Citations76
Published2018
Admission routes1
Has abstractyes

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