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Record W2903763975 · doi:10.1039/c8lc01077c

Dynamic control of capillary flow in porous media by electroosmotic pumping

2018· article· en· W2903763975 on OpenAlexfundno aff
Tally Rosenfeld, Moran Bercovici

Bibliographic record

VenueLab on a Chip · 2018
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
FundersH2020 European Research CouncilAzrieli FoundationEuropean Commission
KeywordsCapillary actionPorous mediumFlow (mathematics)Flow control (data)Electro-osmosisPorosityMechanicsMaterials scienceEnvironmental scienceChromatographyChemistryComputer scienceComposite materialPhysicsTelecommunicationsElectrophoresis

Abstract

fetched live from OpenAlex

Microfluidic paper-based analytical devices (μPADs) rely on capillary flow to achieve filling, mixing and delivery of liquids. We investigate the use of electroosmotic (EO) pumping as a mechanism for dynamic control of capillary flow in paper-based devices. The applied voltage can accelerate or decelerate the baseline capillary-driven velocity, as well as be used to create a tunable valve that reversibly switches the flow on and off in an electrically controlled manner. The method relies on simple fabrication and allows repeated actuation, providing a high degree of flexibility for automation of liquid delivery. We adapt the Lucas-Washburn model to account for EO pumping and provide an experimentally validated analytical model for the distance penetrated by the liquid as a function of time and the applied voltage. We show that the EO-pump can reduce filling time by 6.5-fold for channels spanning several cm in length, relative to capillary filling alone. We demonstrate the utilization of the EO-pump for a tunable and dynamic flow control that accelerates, decelerates and stops the flow on demand. Finally, we present the use of the EO-pump for fluid flow sequencing on a paper-based device.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.324

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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