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Record W4407035221 · doi:10.1122/8.0000890

Magnetorheological response of homogeneous and Janus iron-oxide coated silica particles

2025· article· en· W4407035221 on OpenAlexafffund
Samin Habibi, Steven L. Bryant, Roman Shor, Giovanniantonio Natale

Bibliographic record

VenueJournal of Rheology · 2025
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceHomogeneousMagnetorheological fluidJanusComposite materialIron oxideOxideRheologyNanotechnologyMagnetic fieldMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

The flow behavior of magnetic suspensions made of nonmagnetic spherical particles surface-decorated by a magnetic shell is investigated in this study. Suspensions of SiO2@Fe3O4 homogeneous particles (HPs) (the so-called core/shell particles) and SiO2@Fe3O4 Janus particles (JPs) were compared in terms of their rheological properties. Particle surface characteristics and the contribution of anisotropic magnetization of JPs to interparticle interactions were investigated for the first time. Higher shear viscosity, shear stress, and viscoelastic modulus were obtained in magnetic HP suspensions. However, upon elimination of the saturation magnetization effect of particles, higher shear viscosity was achieved in magnetic JP suspensions due to the more significant contribution of the contact force in the JP systems. The dependence of the magnetorheological (MR) properties on the magnetic particle concentration and magnetic field strength was also evaluated for HP and JP suspensions. These two magnetic systems deviate from conventional MR fluids because the magnetization is generated by the magnetic shell instead of the core of the particles. These observations provide new insights and opportunities for designing MR fluids.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.005
GPT teacher head0.212
Teacher spread0.206 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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