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Record W2016058599 · doi:10.1063/1.4915096

Examination of the magnetism dynamics from intermixing effects in <i>γ</i>-Fe2O3/MnO core-shell nanoparticles

2015· article· en· W2016058599 on OpenAlexafffund
Elizabeth Skoropata, Teng Su, Hao Ouyang, J. W. Freeland, J. van Lierop

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Synthesis of Ferrites
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetismMaterials scienceNanoparticleRelaxation (psychology)SpectroscopyShell (structure)Chemical physicsMössbauer spectroscopyMagnetic nanoparticlesInner coreCondensed matter physicsChemistryCrystallographyNanotechnologyPhysicsComposite material

Abstract

fetched live from OpenAlex

We have examined the effects of core-shell intermixing on the dynamical magnetism of γ-Fe2O3/MnO nanoparticles. The core and shell phases were identified using x-ray diffraction, and x-ray absorption spectroscopy identified Mn ions in both octahedral and tetrahedral sites, consistent with a significant amount of substitution at the core-shell interface to form an Fe/Mn-ferrite. The dynamical response was probed by Mössbauer spectroscopy, which decouples surface and core spins, and suggested a change in the relaxation behaviour among the spin populations within γ-Fe2O3/MnO relative to the γ-Fe2O3 seed particles. Interestingly, the magnetic relaxation effects at the atomic scale, measured via Mössbauer spectroscopy, were enhanced, indicating that the addition of an MnO shell and intermixing affected the dynamical freezing process which altered the surface magnetism of the γ-Fe2O3 core. Our results show that both the MnO shell and the interfacial intermixed layer are important in determining the core-shell nanoparticle magnetism.

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 head0.019
GPT teacher head0.215
Teacher spread0.195 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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