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Record W2903948337 · doi:10.1088/1361-6463/aaf780

Quasiperiodic spin waves in bi-component magnonic crystal arrays of nanowires

2018· article· en· W2903948337 on OpenAlexafffund
Bushra Hussain, M. G. Cottam, Baolai Ge

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuasiperiodic functionNanowireComponent (thermodynamics)QuasicrystalCondensed matter physicsSpin waveCrystal (programming language)Spin (aerodynamics)Materials sciencePhysicsOptoelectronicsQuantum mechanicsFerromagnetismComputer science

Abstract

fetched live from OpenAlex

Abstract A theoretical analysis is given for the compositional effects of quasiperiodicity on the dipole-exchange spin waves (SWs) in bi-component magnonic arrays. The investigations are applied to lateral arrays of ferromagnetic nanowire stripes of cobalt and permalloy as the two building blocks, separated by nonmagnetic spacers. The growth rule applied to the building blocks is taken to be in accordance with either the Fibonacci or Thue–Morse quasiperiodic sequence, which provide contrasting results for the spin-wave properties. The spectra of the spin-wave bandgaps and allowed bands for the bicomponent arrays are calculated up to high generation numbers of the quasiperiodic sequences using a Hamiltonian-based microscopic method. The fractal-like scaling properties of the SWs in these structures are studied as a function of wave vector, which affects the relative importance of the dipolar and exchange terms in the two magnetic materials. Comparisons are made for the density of spin-wave states for the quasiperiodic structures with those for periodic and random lateral arrays, showing that the quasiperiodic arrays have distinctive characteristics.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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