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Interface exchange coupling in Co nanoparticles dispersed in a Mn matrix

2010· article· en· W1985277207 on OpenAlexaff
C. Binns, Neus Domingo, A. M. Testa, D. Fiorani, K. N. Trohidou, Marianna Vasilakaki, J. A. Blackman, Abu Asaduzzaman, S Baker, M. Roy, Davide Peddis

Bibliographic record

VenueJournal of Physics Condensed Matter · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInterface (matter)Coupling (piping)NanoparticleMaterials scienceMatrix (chemical analysis)Chemical engineeringNanotechnologyComposite materialEngineering

Abstract

fetched live from OpenAlex

The structural and magnetic properties of 1.8 nm Co particles dispersed in a Mn matrix by co-depositing pre-formed mass-selected Co clusters with an atomic vapour of Mn onto a common substrate have been studied by using EXAFS (extended x-ray absorption fine structure), XMCD (x-ray magnetic circular dichroism), magnetometry, and theoretical modelling. At low Co volume fraction (5%) Co@Mn shows a significant degree of alloying and the well-defined particles originally deposited become centres of high Co concentration CoMn alloy that evolves from pure Co at the nanoparticle centre to the pure Mn matrix within a few nm. Each inhomogeneity is a core-shell particle with a Co-rich ferromagnetic core in contact with a Co-depleted antiferromagnetic shell. The XMCD reveals that the Co moment localized on the Co atoms within the Co-rich cores is much smaller than the ferromagnetic moment of the Co nanoparticles deposited at the same volume fraction in Ag. Electronic structure calculations indicate that the small magnitude of the core Co moment can be understood only if significant alloying occurs. Monte Carlo modelling replicates the exchange bias (EB) behaviour observed at low temperature from magnetometry measurements. We ascribe EB to the interaction between the ferromagnetic Co-rich cores and the antiferromagnetic Mn-rich shells.

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 score0.100
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)0.0020.000

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 head0.011
GPT teacher head0.266
Teacher spread0.256 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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