MétaCan
Menu
Back to cohort
Record W1996079355 · doi:10.3390/mi3010001

Displacement Pumping of Liquids Radially Inward on Centrifugal Microfluidic Platforms in Motion

2011· article· en· W1996079355 on OpenAlexaff
Matthew C. R. Kong, Adam P. Bouchard, Eric D. Salin

Bibliographic record

VenueMicromachines · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrofluidicsCentrifugal pumpRotation around a fixed axisCentrifugal forceFabricationDisplacement (psychology)Volume (thermodynamics)Positive displacement meterMechanical engineeringMaterials scienceRotation (mathematics)MechanicsRotational speedEngineeringComputer scienceNanotechnologyPhysicsImpellerThermodynamics

Abstract

fetched live from OpenAlex

We describe two novel centrifugal microfluidic platform designs that enable passive pumping of liquids radially inward while the platform is in motion. The first design uses an immiscible liquid to displace an aqueous solution back towards the center of the platform, while the second design uses an arbitrary pumping liquid with a volume of air between it and the solution being pumped. Both designs demonstrated the ability to effectively pump 55% to 60% of the solution radially inward at rotational frequencies as low as 400 rpm (6.7 Hz) to 700 rpm (11.7 Hz). The pumping operations reached completion within 120 s and 400 s respectively. These platform designs for passive pumping of liquids do not require moving parts or complex fabrication techniques. They offer great potential for increasing the number of sequential operations that can be performed on centrifugal microfluidic platforms, thereby reducing a fundamental limitation often associated with these platforms.

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 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 score0.006
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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.

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

Citations26
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMicromachinesSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207