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Record W4391344823 · doi:10.3390/nano14030286

Spin Waves in Ferromagnetic Nanorings with Interfacial Dzyaloshinskii–Moriya Interactions: II. Directional Effects

2024· article· en· W4391344823 on OpenAlexafffund
Bushra Hussain, M. G. Cottam

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFerromagnetismCondensed matter physicsSpin waveSpin (aerodynamics)PhysicsMaterials science

Abstract

fetched live from OpenAlex

A theory is presented to study the effect of interfacial Dzyaloshinskii-Moriya interactions (DMIs) on the static and dynamic magnetic properties in single-layered ferromagnetic nanorings. A microscopic (Hamiltonian-based) approach is used that also includes the antisymmetric DMI besides the competing symmetric (bilinear) exchange interactions, magnetic dipole-dipole interactions, and an applied magnetic field. Here, the axial vector of the DMI is taken to be in the plane of the nanoring (by contrast with earlier studies) and we explore cases where it is either parallel or perpendicular to the in-plane magnetic field. Significantly, with this orientation for the DMI axial vector, the inhomogeneous static magnetization is tilted to have a component perpendicular to the plane giving a surface texture. This effect is studied in both the low-field vortex and high-field onion states. There is a consequent modification to the discrete set of spin-wave modes in both states through their frequencies and spatial amplitudes. We present combined analytical and numerical results for the static properties and dynamical magnetization in ferromagnetic nanorings, including the variation with applied field.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.997

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)0.0040.000

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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