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Record W2057836322 · doi:10.1021/la0608866

Colloidal Interactions between Langmuir−Blodgett Bitumen Films and Fine Solid Particles

2006· article· en· W2057836322 on OpenAlexaff
Jun Long, Liyan Zhang, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLangmuir–Blodgett filmColloidSolid surfaceAsphaltChemical engineeringMaterials scienceColloidal particleNanotechnologyChemistryMonolayerOrganic chemistryChemical physicsComposite materialEngineering

Abstract

fetched live from OpenAlex

In oil sand processing, accumulation of surface-active compounds at various interfaces imposes a significant impact on bitumen recovery and bitumen froth cleaning (i.e., froth treatment) by altering the interfacial properties and colloidal interactions among various oil sand components. In the present study, bitumen films were prepared at toluene/water interfaces using a Langmuir-Blodgett (LB) upstroke deposition technique. The surface of the prepared LB bitumen films was found to be hydrophobic, comprised of wormlike aggregates containing a relatively high content of oxygen, sulfur, and nitrogen, indicating an accumulation of surface-active compounds in the films. Using an atomic force microscope, colloidal interactions between the LB bitumen films and fine solids (model silica particles and clay particles chosen directly from an oil sand tailing stream) were measured in industrial plant process water and compared with those measured in simple electrolyte solutions of controlled pH and divalent cation concentrations. The results show a stronger long-range repulsive force and weaker adhesion force in solutions of higher pH and lower divalent cation concentration. In plant process water, a moderate long-range repulsive force and weak adhesion were measured despite its high electrolyte content. These findings provide more insight into the mechanisms of bitumen extraction and froth treatment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.477

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 head0.007
GPT teacher head0.223
Teacher spread0.215 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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