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Record W2108126249 · doi:10.1039/c3sm51403j

Interfacial deflection and jetting of a paramagnetic particle-laden fluid: theory and experiment

2013· article· en· W2108126249 on OpenAlexaff
Scott Tsai, Ian M. Griffiths, Zhenzhen Li, Pilnam Kim, Howard A. Stone

Bibliographic record

VenueSoft Matter · 2013
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsToronto Metropolitan University
FundersEngineering and Physical Sciences Research CouncilKing Abdullah University of Science and TechnologyNational Science Foundation
KeywordsFerrofluidMagnetDeflection (physics)MechanicsParamagnetismMaterials scienceMagnetic nanoparticlesMagnetic fieldScalingParticle (ecology)Magnetic particle inspectionJet (fluid)Deformation (meteorology)PhysicsCondensed matter physicsClassical mechanicsNanotechnologyGeometryComposite materialNanoparticleGeology

Abstract

fetched live from OpenAlex

We describe the results of experiments and mathematical analysis of the deformation of a free surface by an aggregate of magnetic particles. The system we study is differentiated from ferrofluid systems because it contains regions rich with magnetic material as well as regions of negligible magnetic content. In our experiments, the magnetic force from a spherical permanent magnet collects magnetic particles to a liquid–air interface, and deforms the free surface to form a hump. The hump is composed of magnetic and non-magnetic regions due to the particle collection. When the magnet distance falls below a threshold value, we observe the transition of the hump to a jet. The mathematical model we develop, which consists of a numerical solution and an asymptotic approximation, captures the shape of the liquid–air interface during the deformation stage and a scaling prediction for the critical magnet distance for the hump to become a jet.

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 score0.147
Threshold uncertainty score0.620

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)0.0010.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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