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Record W1997082455 · doi:10.4236/jbise.2014.79064

Helical Dielectrophoretic Particle Separator Fabricated by Conformal Spindle Printing

2014· article· en· W1997082455 on OpenAlexaff
Nicolas Guérin, Martin Lévesque, Daniel Therriault

Bibliographic record

VenueJournal of Biomedical Science and Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSeparator (oil production)Materials scienceDielectrophoresisElectric fieldMicrofluidicsRadius of curvatureFabricationVoltageElectrokinetic phenomenaConformal mapOptoelectronicsMicrochannelCurvatureComposite materialNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

This paper reports the fabrication and testing of a helical cell separator that uses insulator-based dielectrophoresis as the driving force of its separation. The helical channel shape’s main advantage is its constant curvature radius which generates a constant electric field gradient. The presented separator was fabricated by extruding a sacrificial ink on rotating spindles using a computer-controlled robot. After being assembled, connected to the reservoir and encapsulated in epoxy resin, the ink was removed to create a helical microchannel. The resulting device was tested by circulating polystyrene microbeads of 4 and 10 μm diameter through its channel using a voltage of 900 VDC. The particles were separated with efficiencies of 94.0% and 92.5%, respectively. However, roughness in some parts of the channel and connections that had larger diameters compared to the channel created local electric field gradients which, doubtless, hindered separation. It is a promising device that could lead the way toward portable and affordable medical devices.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.003
GPT teacher head0.181
Teacher spread0.178 · 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 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

Citations9
Published2014
Admission routes1
Has abstractyes

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