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Record W1584093194 · doi:10.1109/ias.1990.152273

Liquid-liquid dispersion under pulsed electric fields in a horizontal cell

2002· article· en· W1584093194 on OpenAlexafffund
Wenqi He, J.S. Chang, M. H. I. Baird

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNozzleContactorElectric fieldDispersion (optics)Distilled waterMaterials scienceVoltageMechanicsPhase (matter)Analytical Chemistry (journal)ChemistryOpticsPower (physics)ChromatographyMechanical engineeringElectrical engineeringPhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Liquid-liquid reactors require good agitation and large interfacial area, as does liquid-liquid extraction equipment. Conventionally, droplet dispersion is obtained by mechanical agitation, for example, in stirred tanks. An alternative approach involving much less energy consumption is to promote droplet dispersion and motion by applying an electric field. The formation and initial motion of drops provide the focus for this study. The apparatus used was a nearly horizontal rectangular cell (38*10*10 cm) equipped with parallel electrode plates along the two sides of the cell as well as a stainless steel nozzle (0.71 mm diameter) connected to a high-voltage pulsed electric power supply. The experiments were carried out in this liquid-liquid contactor by generating a series of single droplets of distilled water from the grounded nozzle and observing their subsequent motion in the viscous continuous phase under the influence of the pulsed electric field. As the applied voltage was increased, the formed droplets were reduced in size and showed repulsion and some upwards scattering: the droplet velocity near the nozzle was greatly increased by the field, with the droplets decelerating as they moved away from the nozzle.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.724

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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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 designSimulation or modeling
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

Citations0
Published2002
Admission routes2
Has abstractyes

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