MétaCan
Menu
Back to cohort
Record W1994427366 · doi:10.1063/1.4919269

Investigating nanoparticle interactions from interparticle-to-nanocomposite

2015· article· en· W1994427366 on OpenAlexafffund
R. D. Desautels, Elizabeth Skoropata, Michael P. Rowe, J. van Lierop

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanoparticleNanocompositeMaterials scienceMagnetizationMagnetic nanoparticlesExchange biasShell (structure)Magnetic anisotropySinteringDipoleOxideIron oxide nanoparticlesCondensed matter physicsChemical physicsNanotechnologyChemical engineeringComposite materialChemistryMetallurgyMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

A series of core/shell Fe-oxide/SiO2 nanoparticles were produced to form Fe-oxide (1:1 Fe3O4 and γ-Fe2O3) cores with diameters of approximately 4.6 nm and shell thicknesses ranging from 3.6 to 5.4 nm. Hot press sintering of core/shell nanoparticles created a nanocomposite of Fe-oxide nanoparticles in a SiO2 matrix. The presence of an iron-orthosilicate at the core-shell interface defines the intrinsic magnetic properties of the nanoparticle systems, resulting in an increase in magnetic anisotropy with thicker SiO2 shell. We find that dipole-dipole interactions are mediated by the overall SiO2 shell, and that these interactions are coupling neighbouring particles' magnetization with increasing correlation lengths. When the nanoparticles from a composite material, packing significantly increases the interaction strengths, altering the overall magnetization of the system so that the iron-oxide cores present an approximate 25% increase in (saturation) magnetization to a bulk-like (∼80 emu/g) value.

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 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 score0.078
Threshold uncertainty score0.474

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)0.0000.000

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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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.

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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicMagnetic properties of thin filmsFrench-language works237,207