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Record W4240199746 · doi:10.1063/1.4982969.1

10.1063/1.4982969.1

2017· dataset· en· W4240199746 on OpenAlexaff
Hai Jiang, Na Fan, Bei Peng, Xuan Weng

Bibliographic record

VenueDefault Digital Object Group · 2017
Typedataset
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMicrochannelElectric fieldMechanicsMicrofluidicsPressure dropFlow (mathematics)ElectrodeVoltageMaterials scienceOsmotic pressureChemistryNanotechnologyElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The electro-osmotic pumping and pressure-driven manipulation of fluids are considered as the most common strategies in microfluidic devices. However, both of them exhibit major disadvantages such as hard integration and high reagent consumption, and they are destructive methods for detection and photo bleaching. In this paper, an electric field-effect flow control approach, combining the electro-osmotic pumping force and the pressure-driven pumping force, was developed to generate the induced pressure-driven flow in a T-shaped microfluidic chip. Electro-osmotic flow between the T-intersection and two reservoirs was demonstrated, and it provided a stable, continuous, and electric field-free flow in the section of the microchannel without the electrodes. The velocity of the induced pressure-driven flow was linearly proportional to the applied voltages. Both numerical and experimental investigations were conducted to prove the concept, and the experimental results showed good agreement with the numerical simulations. In comparison to other induced pressure pumping methods, this approach can induce a high and controllable pressure drop in the electric field-free segment, subsequently causing an induced pressure-driven flow for transporting particles or biological cells. In addition, the generation of bubbles and the blocking of the microchannel are avoided.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.021

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 head0.007
GPT teacher head0.222
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2017
Admission routes1
Has abstractyes

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