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Two-phase continuum theory of magnetically induced heating of colloidal suspensions in capillary flow

2025· article· en· W4413407395 on OpenAlexafffund
Zakaria Larbi, Faı̈çal Larachi, Seyed Mohammad Taghavi, Abdelwahid Azzi

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapillary actionMaterials scienceMechanicsColloidTwo-phase flowColloidal particleFlow (mathematics)Phase (matter)ThermodynamicsPhysicsComposite materialChemical engineering

Abstract

fetched live from OpenAlex

This study presents a two-phase continuum model for heat generation and transfer in magnetic colloidal suspensions, distinguishing the thermal behavior of the liquid and nanoparticle phases. It incorporates the magnetocaloric effect, where nanoparticles align under time-dependent magnetic fields, generating heat via magnetic entropy reduction. Two field types are examined: oscillating (OMF), which does not induce flow, and rotating (RMF), which induces spin-up flow. The magnetocaloric effect is frequency-dependent and becomes significant when the field period is shorter than the Brownian relaxation time. This leads to fluid heating and sustained alignment of nanoparticle magnetic moments with the RMF. A parametric study shows that field strength, nanoparticle concentration, and size influence thermal output. RMF produces nearly twice the heat of OMF at equal frequency and amplitude. These results indicate that magnetocaloric heating under rotating fields can simultaneously supply thermal energy and promote mixing, which may benefit reactions in microfluidic systems without direct contact or embedded heaters.

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 score0.029
Threshold uncertainty score0.252

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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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