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Record W2075133500 · doi:10.1039/c2lc21045b

Cell separation based on size and deformability using microfluidic funnel ratchets

2012· article· en· W2075133500 on OpenAlexafffund
Sarah M. McFaul, Bill K. Lin, Hongshen Ma

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMicrofluidicsFiltration (mathematics)FunnelRatchetSortingCell sizeMaterials scienceCell sortingFilter (signal processing)Mechanism (biology)Biological systemNanotechnologyMechanicsChemistryComputer scienceMechanical engineeringCellPhysicsBiologyEngineeringWork (physics)Mathematics

Abstract

fetched live from OpenAlex

The separation of biological cells by filtration through microstructured constrictions is limited by unpredictable variations of the filter hydrodynamic resistance as cells accumulate in the microstructure. Applying a reverse flow to unclog the filter will undo the separation and reduce filter selectivity because of the reversibility of low-Reynolds number flow. We introduce a microfluidic structural ratchet mechanism to separate cells using oscillatory flow. Using model cells and microparticles, we confirmed the ability of this mechanism to sort and separate cells and particles based on size and deformability. We further demonstrate that the spatial distribution of cells after sorting is repeatable and that the separation process is irreversible. This mechanism can be applied generally to separate cells that differ based on size and deformability.

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)0.001
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: 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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations228
Published2012
Admission routes2
Has abstractyes

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