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Record W2945701625 · doi:10.1098/rspa.2018.0852

Methodology to calculate interfacial tension under electric field using pendent drop profile analysis

2019· article· en· W2945701625 on OpenAlexfundno aff
Sameer Mhatre, Sébastien Simon, Johan Sjöblom

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsnot available
FundersNorges ForskningsrådBranco Weiss Fellowship – Society in ScienceUniversity of Alberta
KeywordsSurface tensionElectric fieldLaplace's equationDrop (telecommunication)Laplace transformAdsorptionMechanicsChemistryThermodynamicsMaterials sciencePhysicsBoundary value problemMathematicsPhysical chemistryMathematical analysisMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, we present a methodology to calculate interfacial tension of a water-oil interface under an electric field. The Young-Laplace equation, conventionally used to estimate surface/interfacial tension in axisymmetric drop shape analysis (ADSA), is modified to include electrostatic effects. The solution needs normal component of the Maxwell stress at the interface which is calculated separately by solving the Laplace equation for electric potential. The optimized fitting between the resulting theoretical profile and the experimentally obtained profile results into Bond number which is used to calculate the apparent value of interfacial tension. The algorithm can process a large number of drop profiles in one go. The methodology can be applied in the ADSA studies for adsorption dynamics where a drop is held for a long time while surface active molecules are allowed to adsorb. The method discussed in this paper will help the future studies in adsorption dynamics at fluid interfaces under electric field and the resulting interfacial property evolution.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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