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Record W2783280667 · doi:10.1021/acs.jpcb.7b10691

Electrokinetic Motion of an Oil Droplet Attached to a Water–Air Interface from Below

2018· article· en· W2783280667 on OpenAlexafffund
Chengfa Wang, Yongxin Song, Xinxiang Pan, Dongqing Li

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2018
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaGovernment of Canada
KeywordsElectrokinetic phenomenaZeta potentialElectric fieldViscosityMechanicsStreaming currentRADIUSOil dropletChemistryMaterials scienceAnalytical Chemistry (journal)NanotechnologyComposite materialChromatographyPhysicsEmulsion

Abstract

fetched live from OpenAlex

The electrokinetic motion of a negatively charged oil droplet attached to the negatively charged water-air interface from below is numerically studied for the first time by a three-dimensional numerical model in this paper. The effects of the mobile water-air interface and the mobile water-oil interface on the electrokinetic motion of the attached oil droplet are investigated and discussed in terms of the zeta potentials at the water-air interface and the oil droplet surface, the applied electric field, the dynamic viscosity ratio of oil to water, and the droplet radius. The results show that the negatively charged oil droplet attached to the negatively charged water-air interface from below moves in the opposite direction to that of the external electric field, and its moving velocity increases with the increase of the electric field strength, the magnitudes of the zeta potentials at both the water-air interface and the water-oil interface, and the droplet size, as well as the dynamic viscosity ratio of oil to water.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.264
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2018
Admission routes2
Has abstractyes

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