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Fractionation of Asphaltenes in Understanding Their Role in Petroleum Emulsion Stability and Fouling

2016· article· en· W2564986322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesNorges Forskningsråd
KeywordsAsphalteneFractionationAdsorptionChemistryEmulsionSolubilityPetroleumChemical engineeringCrude oilChromatographyDispersantSolvophobicOrganic chemistryMoleculeDispersion (optics)Petroleum engineeringGeology

Abstract

fetched live from OpenAlex

SARA fractionation separates crude oil into fractions of saturates (S), aromatics (A), resins (R), and asphaltenes (A) based on the differences in their polarizability and polarity. Defined as a solubility class, asphaltenes are normally considered as a nuisance in the petroleum industry mainly as a result of their problematic precipitation and adsorption at oil–water and oil–solid interfaces. Because a broad range of molecules fall within the group of asphaltenes with distinct sizes and structures, considering the asphaltenes as a whole was noted to limit the deep understanding of governing mechanisms in asphaltene-induced problems. Extended-SARA (E-SARA) is proposed as a concept of asphaltene fractionation according to their interfacial activities and adsorption characteristics, providing critical information to correlate specific functional groups with certain characteristics of asphaltene aggregation, precipitation, and adsorption. Such knowledge is essential to addressing asphaltene-related problems by targeting specific subfractions of asphaltenes.

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.

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 score0.068
Threshold uncertainty score0.281

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 head0.023
GPT teacher head0.237
Teacher spread0.214 · 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