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Record W2325214859 · doi:10.1021/ef400142v

Molecular Weight and Density Distributions of Asphaltenes from Crude Oils

2013· article· en· W2325214859 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.

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersShell
KeywordsAsphalteneHeptaneTolueneChemistryMolar mass distributionSolubilityChromatographyOrganic chemistryAnalytical Chemistry (journal)ThermodynamicsPolymer

Abstract

fetched live from OpenAlex

Asphaltenes self-associate, and the molecular weight and density distributions are a factor in asphaltene precipitiation. To determine these distributions, heptane-extracted asphaltenes from four crude oils were fractionated into solubility cuts. The asphaltenes were dissolved in toluene and then partially precipitated at specified ratios of heptane/toluene to generate sets of light (soluble) and heavy (insoluble) cuts. The molecular weight and density were measured for each cut. The asphaltenes were found to include both associating and non-associating asphaltenes. The content of non-associating components was up to 15 wt % of the asphaltenes. The density distributions were determined directly from the data. The molecular weight data were fitted with a self-association model to predict the distributions at any given concentration. Then, a guideline was developed to represent the molecular weight distribution of non-associated and associated asphaltenes with a Γ distribution function. Finally, the density of asphaltene cuts was correlated to their molecular weight. This correlation fit the data with an average absolute deviation of 11 kg/m 3 .

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 score0.013
Threshold uncertainty score0.625

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)0.0010.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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