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Record W2315861082 · doi:10.1021/ie1020798

A Novel Centrifugal Method for Wettability Characterization of Granulates

2011· article· en· W2315861082 on OpenAlexaff
Paul D. Swenson, Géza Horváth‐Szabó, Wael Abdallah, Dmitry Eskin

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsContact angleWettingCentrifugal forceMaterials scienceSodium dodecyl sulfateCharacterization (materials science)Chemical engineeringOil dropletComposite materialChemistryChromatographyNanotechnologyEmulsionRotational speedMechanical engineering

Abstract

fetched live from OpenAlex

The minimum force needed for transporting glass microspheres across oil/water (O/W) interfaces was determined experimentally. From this data, the O/W contact angle was calculated with a mathematical model. First, the measured minimum centrifugal force was combined with a simplified model leading to an approximate contact angle. Then the results were refined by a numerical solution of the accurate mathematical model of particle transport through an oil/water interface. The contact angles calculated by the suggested approach were compared with the contact angles measured by goniometry on flat glass surfaces in the presence and absence of sodium dodecyl sulfate (SDS), Triton X-100, and Igepal CO-520 surfactants. The discrepancies were interpreted with the different surface chemistries/morphologies of glass microspheres and slides. The results show potential for this being a rapid screening method for chemical additives aimed at altering the wettability. This approach could be utilized in the oil and gas industry and especially to support enhanced oil recovery (EOR).

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.129
GPT teacher head0.337
Teacher spread0.209 · 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
GenreMethods

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

Citations3
Published2011
Admission routes1
Has abstractyes

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