MétaCan
Menu
Back to cohort
Record W2324249937 · doi:10.1021/la5039206

Spontaneously Formed Interfacial Metal Silicates and Their Effect on the Magnetism of Superparamagnetic FeCo/SiO<sub>2</sub>Core/Shell Nanoparticles

2015· article· en· W2324249937 on OpenAlexafffund
R. D. Desautels, Michael P. Rowe, Michael Jones, Amanda Whallen, J. van Lierop

Bibliographic record

VenueLangmuir · 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 InnovationToyota USA
KeywordsSuperparamagnetismX-ray photoelectron spectroscopyMagnetismMaterials scienceNanoparticleX-ray absorption spectroscopyNanomaterialsSilicateMetalAbsorption spectroscopyShell (structure)Nanoscopic scaleChemical engineeringChemical physicsNanotechnologyMagnetizationChemistryCondensed matter physicsMetallurgyMagnetic fieldComposite materialOptics

Abstract

fetched live from OpenAlex

The integration of superparamagnetic core/shell nanoparticles into devices and other nanoscale technological applications requires a detailed understanding of how the intimate contact between core and shell nanophases affects the magnetism. We report how, for single-domain FeCo nanoparticles, an FeCo phase unique to the nanoscale with silica shells of increasing thicknesses spontaneously formed interfacial metal silicates between the core and shell (such as Fe2SiO4 and Co2SiO4) and altered the overall magnetism of the nanomaterial significantly. The influence of this previously overlooked phenomenon on magnetic properties is reported. Evidence of these metal silicate interfacial layers was observed by X-ray absorption spectroscopy (XAS) collected over the L3,2 absorption edges of Fe and X-ray photoelectron spectra (XPS) collected over the 2p transitions of Fe and Co. Through the correlation of magnetometry and XPS data, the evolution of nanoparticle magnetic anisotropy is shown to increase with the metal silicate.

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 score0.057
Threshold uncertainty score0.614

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 head0.016
GPT teacher head0.206
Teacher spread0.190 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueLangmuirSame topicMagnetic properties of thin filmsFrench-language works237,207