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Record W3117574635 · doi:10.1109/tim.2020.3031212

Detection Mechanism of Water Content in Oil–Water Emulsions by Coaxial Double Cylinder Electrodes

2020· article· en· W3117574635 on OpenAlexaff
Meiyi Qing, Huaqing Liang, Jinjun Zhang, Hamid Esmaeili Najafabadi, Honglei Zhan, Henry Leung

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2020
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Calgary
FundersChina University of Petroleum, BeijingNational Natural Science Foundation of China
KeywordsCoaxialElectrodeMaterials scienceCylinderElectrical impedanceWater contentEquivalent circuitEmulsionSystem of measurementAcousticsElectrical engineeringVoltageMechanical engineeringEngineeringChemistryGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

This article proposes a new method for detecting water content in oil-water emulsions by a coaxial double cylinder electrodes system. An equivalent circuit model of the system is developed that consists of the electrical double layers, sample solutions, and electrodes. The impedance response is analyzed for different frequency ranges, and the effect of the electrode surface-sample solution interface is also investigated. A coaxial double cylinder electrodes system is designed and fabricated to conduct measurement experiments. The results verify the validity of the model and uncover a linear relationship between the water content and the electrical properties. Combined with the back propagation artificial neural network, the impedance spectral data also show a strong correlation with water content. We also demonstrate how the method can contribute to determining the water content of an oil-water emulsion system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.426

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 head0.042
GPT teacher head0.231
Teacher spread0.189 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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