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Record W3091587757 · doi:10.1002/smll.202003612

Tunable Harmonic Flow Patterns in Microfluidic Systems through Simple Tube Oscillation

2020· article· en· W3091587757 on OpenAlexfundno aff
Peter Thurgood, Sergio Aguilera Suarez, Elena Pirogova, Aaron R. Jex, Karlheinz Peter, Sara Baratchi, Khashayar Khoshmanesh

Bibliographic record

VenueSmall · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilAustralian Centre for Electromagnetic Bioeffects ResearchRMIT UniversityAustralian Research CouncilOntario Ministry of Natural Resources and Forestry
KeywordsMicrofluidicsOscillation (cell signaling)HarmonicVortexSimple harmonic motionObstacleFlow (mathematics)Materials scienceNanotechnologyViscosityAcousticsComputer scienceMechanicsPhysicsChemistryClassical mechanics

Abstract

fetched live from OpenAlex

Generation of tunable harmonic flows at low cost in microfluidic systems is a persistent and significant obstacle to this field, substantially limiting its potential to address major scientific questions and applications. This work introduces a simple and elegant way to overcome this obstacle. Harmonic flow patterns can be generated in microfluidic structures by simply oscillating the inlet tubes. Complex rib and vortex patterns can be dynamically modulated by changing the frequency and magnitude of tube oscillation and the viscosity of liquid. Highly complex rib patterns and synchronous vortices can be generated in serially connected microfluidic chambers. Similar dynamic patterns can be generated using whole or diluted blood samples without damaging the sample. This method offers unique opportunities for studying complex fluids and soft materials, chemical synthesis of various compounds, and mimicking harmonic flows in biological systems using compact, tunable, and low-cost 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 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 score0.279
Threshold uncertainty score0.648

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 head0.033
GPT teacher head0.206
Teacher spread0.173 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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