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Record W2062627094 · doi:10.1002/cjce.20541

Computational fluid dynamics simulation of a quadrupole magnetic sorter flow channel: Effect of splitter position on nonspecific crossover

2011· article· en· W2062627094 on OpenAlexvenueno aff
Venkata Sunil Kumar Sajja, David J. Kennedy, Paul Todd, Thomas R. Hanley

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesU.S. Department of Health and Human Services
KeywordsSplitterCrossoverPosition (finance)Channel (broadcasting)QuadrupolePhysicsFlow (mathematics)MechanicsDynamics (music)Open-channel flowComputational fluid dynamicsOpticsAcousticsComputer scienceTelecommunicationsAtomic physicsArtificial intelligence

Abstract

fetched live from OpenAlex

In the Quadrupole Magnetic Sorter (QMS) magnetic particles enter a vertical flow annulus and are separated from non-magnetic particles by radial deflection into an outer annulus where the purified magnetic particles are collected via a flow splitter. The purity of magnetically isolated particles in QMS is affected by the migration of nonmagnetic particles across transport lamina in the annular flow channel. Computational Fluid Dynamics (CFD) simulations were used to predict the flow patterns, pressure drop and nonspecific crossover in QMS flow channel for the isolation of pancreatic islets of Langerhans. Simulation results were compared with the experimental results to validate the CFD model. Results of the simulations were used to show that one design gives up to 10% less nonspecific crossover than another and this model can be used to optimise the flow channel design to achieve maximum purity of magnetic particles.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.178
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2011
Admission routes1
Has abstractyes

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