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Record W2094891080 · doi:10.1115/icnmm2008-62365

Experimental Study of Particle-Liquid Flow Behaviour in a Microchannel Attached to an Inlet Reservoir

2008· article· en· W2094891080 on OpenAlexaff
Clement K. M. Kong, Sasan Raghibizadeh, Masahiro Kawaji

Bibliographic record

VenueASME 2008 6th International Conference on Nanochannels, Microchannels, and Minichannels · 2008
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMicrochannelParticle (ecology)Volumetric flow rateParticle sizeMaterials sciencePolystyreneAnalytical Chemistry (journal)MechanicsChemistryChromatographyComposite materialNanotechnologyPhysicsGeologyPolymer

Abstract

fetched live from OpenAlex

An experimental study of a solid particle-liquid mixture in a microchannel was conducted by injecting a particle-liquid mixture into a 3.0 mm diameter cylindrical reservoir connected to a square microchannel. Glass particles with diameters ranging from 8 to 12 μm and density between 0.95 and 1.15g/ml, as well as polystyrene particles with diameters ranging from 8 to 50μm and an average density of 1.03–1.05 g/ml were mixed with de-ionized water to create particle-liquid mixtures. The mixture could be continuously stirred in the reservoir using a small magnetic stir bar. The number of particles entering the microchannel was found to depend on the concentration and density of the particles in the reservoir, stirring and fluid flow rates. The particles with a density larger than the carrier fluid entered the microchannel, settled at the bottom of the channel and became immobile at low fluid flow rates, blocking the subsequent particle flow. In many cases, the particles flowed in small groups or clusters, which were separated by short distances. The average distance between the particles or particle clusters was found to vary both with the stirring rate in the reservoir as well as the fluid flow rate in the microchannel.

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 categoriesMeta-epidemiology (narrow)
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 score0.307
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.075
GPT teacher head0.319
Teacher spread0.244 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

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