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Record W2107856334 · doi:10.1103/physrevb.81.195416

Dynamics of domain growth driven by dipolar interactions in a perpendicularly magnetized ultrathin film

2010· article· en· W2107856334 on OpenAlexaff
Nidal Abu-Libdeh, D. Venus

Bibliographic record

VenuePhysical Review B · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCondensed matter physicsDipoleRelaxation (psychology)PhysicsZeeman effectDomain (mathematical analysis)PerpendicularMagnetic domainDomain wall (magnetism)Materials scienceMagnetic fieldNuclear magnetic resonanceMagnetizationQuantum mechanics

Abstract

fetched live from OpenAlex

Measurements of the ac magnetic susceptibility of perpendicularly magnetized Fe/2 ML Ni/W(110) ultrathin films show a clear signature of the dynamics of domain growth and domain density changes in the domain pattern that this system supports. The susceptibility peak measured at different constant heating rates in the range $0.20\text{ }\text{K}/\text{s}\ensuremath{\le}R\ensuremath{\le}0.70\text{ }\text{K}/\text{s}$ shifts to higher temperature as the heating rate is increased. Analysis using a relaxation model demonstrates quantitatively that the dynamics is driven by a nonequilibrium domain density at (nearly) zero field (i.e., by dipole interactions) and that the temperature shift is due to a response time determined by the pinning of local domain wall segments by structural defects. The fundamental time scale for relaxation of the domain density driven by dipole interactions is of order ${10}^{5}$ times slower than the fundamental time scale for an individual Barkhausen step driven by an applied field. The increase in the fundamental time scale reflects the relative size of dipole and Zeeman energies and the need for the correlated motion of many individual domain wall steps required to affect domain growth.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.999

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 integrity0.0000.001
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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations13
Published2010
Admission routes1
Has abstractyes

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Same venuePhysical Review BSame topicMagnetic properties of thin filmsFrench-language works237,207