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Record W4252386267 · doi:10.1109/ias.1993.299124

Droplet charge-to-mass ratio measurement in an EHD liquid-liquid extraction system

2002· article· en· W4252386267 on OpenAlexaff
Wei He, J.S. Chang, M.H.I. Baird

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrohydrodynamicsElectrodeVoltageElectric fieldFaraday cageMaterials sciencePhase (matter)MechanicsLiquid crystalAnalytical Chemistry (journal)Distilled waterTwo-phase flowFlow (mathematics)ChemistryPhysicsElectrical engineeringChromatographyMagnetic fieldOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

An experimental investigation has been carried out in a rectangular lucite cell equipped with parallel electrode plates along the two sides of the cell. The droplets are formed at a grounded hollow electrode. Distilled water is used as the droplet phase and a viscous mineral oil is used as the continuous phase. Experiments have been conducted at various flow rates of the dispersed phase with quiescent continuous phase; the applied DC voltage is from 0 to 15 kV. Charge acquired on droplets both at the hollow electrode and downstream near the bottom of the cell was observed from hollow electrode current waveforms and by a Faraday-cup through direct sampling, respectively. The results reported from the present investigation, extending from the single discrete droplet regime (at low applied voltage) to the dispersed multidroplet regime (at high applied voltage), indicate that the modified Rayleigh instability model and the Vonnegut and Neubauer model can predict the maximum droplet charge acquired in liquid-liquid systems. The modified Vonnegut model can predict most of the experimental results when the applied electric field is high enough and EHD (electrohydrodynamic) forces become dominant.< <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 score0.882
Threshold uncertainty score0.878

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 head0.017
GPT teacher head0.210
Teacher spread0.193 · 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 routes1
Has abstractyes

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