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Record W2330796504 · doi:10.1021/ef3012189

Occlusion of Polyaromatic Compounds in Asphaltene Precipitates Suggests Porous Nanoaggregates

2012· article· en· W2330796504 on OpenAlexaff
Marzie Derakhshesh, Alexander Bergmann, Murray R. Gray

Bibliographic record

VenueEnergy & Fuels · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsToluenePhenanthreneAsphalteneChemistryPyrenePentanePrecipitationAdsorptionSolventChemical engineeringChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

This study examined the occlusion of two polyaromatic hydrocarbons (PAHs) (pyrene and phenanthrene) in asphaltene precipitates. To test for occlusion inside the nanoaggregates, a toluene solution of asphaltene and each of these aromatic compounds was allowed to equilibrate and mix for 2 days to enable penetration into the asphaltene nanoaggregates and then the asphaltenes were precipitated with n -pentane, filtered, washed, and dried. To test for trapping and adsorption outside the nanoaggregates during precipitation, a solution of asphaltene in toluene was mixed with a solution of aromatic compound in toluene and n -pentane, giving immediate precipitation, then allowed to equilibrate overnight, then filtered, washed, and dried. The PAHs in the asphaltene precipitates were determined quantitatively by gas chromatography using a high-temperature simulated distillation instrument. Pyrene and phenanthrene, which are normally soluble in the toluene– n -pentane solutions, were detected in the asphaltene precipitates at up to 6 wt % concentration. Trapping of PAHs outside of the nanoaggregates during precipitation gave 7–14 times less of the PAHs in the solid precipitate. This study shows that asphaltene aggregates can interact significantly with PAHs. The results are consistent with the presence of open porous asphaltene nanoaggregates in solutions, such as toluene.

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 score0.062
Threshold uncertainty score0.697

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 head0.009
GPT teacher head0.234
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

Citations37
Published2012
Admission routes1
Has abstractyes

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