MétaCan
Menu
Back to cohort
Record W2019540596 · doi:10.1039/b613344d

Continuous dielectrophoretic cell separation microfluidic device

2006· article· en· W2019540596 on OpenAlexafffund
Youlan Li, Colin Dalton, H. John Crabtree, Gregory Nilsson, K.V.I.S. Kaler

Bibliographic record

VenueLab on a Chip · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMicralyneUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsFractionationMaterials scienceInjectorSample (material)Lab-on-a-chipFabricationMicrofluidic chipDielectrophoresisChipNanotechnologySample preparationElectrophoresisChromatographyOptoelectronicsChemistryEngineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

We present a prototype microfluidic device developed for the continuous dielectrophoretic (DEP) fractionation and purification of sample suspensions of biological cells. The device integrates three fully functional and distinct units consisting of an injector, a fractionation region, and two outlets. In the sheath and sample injection ports, the cell sample are hydrodynamically focused into a stream of controlled width; in the DEP fractionation region, a specially shaped nonuniform (isomotive) electric field is synthesized and employed to facilitate the separation, and the sorted cells are then delivered to two sample collection ports. The microfluidic behavior of the injector region was simulated and then experimentally verified. The operation and performance of the device was evaluated using yeast cells as model biological particles. Issues relating to the fabrication and operation of the device are discussed in detail. Such a device takes a significant step towards an integrated lab-on-a-chip device, which could interface/integrate to a number of other on-chip components for the device to undertake the whole laboratory procedure.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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

Citations157
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueLab on a ChipSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207