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Record W3035865027 · doi:10.1039/d0lc00333f

Buoyancy-driven step emulsification on pneumatic centrifugal microfluidic platforms

2020· article· en· W3035865027 on OpenAlexafffund
Liviu Clime, Lidija Malic, Jamal Daoud, Luke Lukic, Matthias Geißler, Teodor Veres

Bibliographic record

VenueLab on a Chip · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaCanadian Space Agency
KeywordsBuoyancyMicrofluidicsCentrifugal forceDisplacement (psychology)Mechanical engineeringMechanicsCentrifugal pumpEngineeringMaterials scienceNanotechnologyPhysicsImpeller

Abstract

fetched live from OpenAlex

We present here a new method for controlling the droplet size in step emulsification processes on a centrifugal microfluidic platform, which, in addition to the centrifugal force, uses pneumatic actuation for fluid displacement. We highlight the importance of the interplay between buoyancy effects and the flow rate at the step junction, and provide a simple analytical model relating these two quantities to the size of the droplets. Numerical models as well as experiments with water-in-oil emulsions are performed in support of the proposed model.

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 score0.364
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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