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Record W2074073165 · doi:10.1021/ef9000117

Effect of Solid Wettability on Processability of Oil Sands Ores

2009· article· en· W2074073165 on OpenAlexaff
Trong Dang‐Vu, Rahul Jha, Shiau-Yin Wu, Dwayne D. Tannant, Jacob H. Masliyah, Zhenghe Xu

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsCanadian Natural Resources
Fundersnot available
KeywordsAsphaltWettingOil sandsSessile drop techniqueDrop (telecommunication)PetroleumContact angleMaterials scienceMetallurgyMineralogyGeologyComposite material

Abstract

fetched live from OpenAlex

The wettability of mineral solids and bitumen isolated from nine different Athabasca oil sands ores was determined to establish its role in water-based extraction of bitumen from oil sands. The processability of oil sands ores was determined using Denver flotation tests. The contact angle of a water drop on a bitumen-coated silica wafer was measured using the sessile drop method. For fine solids (<45 μm), the water drop penetration time was measured on a surface of a compressed disk of fine solids. For coarse solids (106−250 μm), the wettability of solids was evaluated by determining the partitioning of the solids between an oil and a water phase. It was found that the processability of different oil sands ores varies significantly in term of bitumen recovery, bitumen froth quality, and bitumen froth morphology. The wettability of bitumen, on the other hand, does not significantly depend on the source of oil sands ores. However, the wettability of the fine and coarse mineral solids is ore-dependent, which affects both bitumen recovery and bitumen froth quality. The presence of hydrophobic solids in oil sands ores depresses bitumen recovery.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.276
Teacher spread0.269 · 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
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

Citations51
Published2009
Admission routes1
Has abstractyes

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