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Irreversible Adsorption of Asphaltenes on Kaolinite: Influence of Dehydroxylation

2017· article· en· W2746778824 on OpenAlexafffund
Qiang Chen, Murray R. Gray, Qi Liu

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsAlberta InnovatesUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesImperial Oil Limited
KeywordsAsphalteneKaoliniteAdsorptionTolueneX-ray photoelectron spectroscopyChemistryChemical engineeringSaturation (graph theory)Inorganic chemistryMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

The adsorption of asphaltenes on dehydroxylated mineral surfaces is an important occurrence during thermal processing of unconventional oil resources such as oil sands bitumen. We investigated the irreversible adsorption of Athabasca asphaltenes from toluene solutions onto untreated and dehydroxylated kaolinite. The adsorption density, percent surface coverage, and mean domain thickness were evaluated using X-ray photoelectron spectroscopy (XPS) and elemental analysis. The dehydroxylated kaolinite gave higher adsorption density and percent surface coverage than the untreated kaolinite, as particularly revealed in multiple contacts of kaolinite with the asphaltene-in-toluene solutions (2 g/L), indicating that dehydroxylation of kaolinite enhanced its adsorption capacity for asphaltenes. The XPS-determined percent surface coverage was 18% and 41% on the untreated and dehydroxylated kaolinite, respectively, corresponding to the maximum adsorption density of 3 and 7 mg/m 2 . The incomplete coverage even at the highest adsorption density proved that the asphaltene adsorption layer was discontinuous on both materials. After saturation of kaolinite binding sites with the first asphaltene layer, at most two additional layers were adsorbed due to asphaltene-on-asphaltene deposition, forming 15 nm thick domains.

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 score0.023
Threshold uncertainty score0.358

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 head0.014
GPT teacher head0.258
Teacher spread0.243 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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