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Record W2312441268 · doi:10.1021/ef2000079

Wettability Alteration of Clay in Solid-Stabilized Emulsions

2011· article· en· W2312441268 on OpenAlexaff
Tianmin Jiang, George J. Hirasaki, Clarence A. Miller, Samson Ng

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSyncrude (Canada)
Fundersnot available
KeywordsKaoliniteWettingEmulsionChemistrySodium silicatePulmonary surfactantChemical engineeringOil sandsAqueous solutionSilicateAsphaltMineralogyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Processing of bitumen froth obtained from surface mining process of Athabasca oil sands yields stable water-in-diluted bitumen emulsions. Even with a demulsifier, a “rag layer” forms between the oil and free water layers when clay solids are present. Experiments reveal that wettability of clay solids has a significant effect on emulsion stability. Kaolinite in toluene–brine mixture was chosen as model system to study clay wettability alteration related to emulsion separation in bitumen froth treatment. Sodium naphthenate was added to simulate the presence of naphthenic acid in diluted bitumen. The fraction of the kaolinite that settled to the bottom of the aqueous phase was measured, and was referred to as “water-wet fraction”, to characterize the wettability of kaolinite. Without any additives, 96% of the kaolinite was water-wet. Addition of only 100 ppm sodium naphthenate reduced the water-wet fraction to only 18%. Wettability of kaolinite was altered by pH control, silicate, and surfactant under different mechanisms. Addition of 366 ppm silicate at pH 10 resulted in 80% of kaolinite being water-wet. To prevent emulsion formation at high pH, cationic and amphoteric surfactants were evaluated as an alternative to alkali. Over 90% of kaolinite became water-wet when adding alkyl quaternary ammonium bromide, betaine, or amine oxide with optimal dosages.

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 score0.048
Threshold uncertainty score0.476

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 head0.242
Teacher spread0.225 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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