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Record W2334652303 · doi:10.1021/ef402065j

Comparison of Hansen Solubility Parameter of Asphaltenes Extracted from Bitumen Produced in Different Geographical Regions

2014· article· en· W2334652303 on OpenAlexaboutno aff
Takashi Sato, Sadao Araki, Masato Morimoto, Ryuzo Tanaka, Hideki Yamamoto

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAsphalteneHildebrand solubility parameterSolubilityChemistryDissolutionDynamic light scatteringAnalytical Chemistry (journal)Particle sizeAsphaltDispersion (optics)ChromatographyChemical engineeringOrganic chemistryMaterials sciencePhysical chemistryComposite materialNanoparticle

Abstract

fetched live from OpenAlex

The Hansen solubility parameters (HSPs) of asphaltenes extracted from oil sand bitumen samples produced at Athabasca in Canada and also from a vacuum residue fraction (VR) produced in the Middle East were determined by the Hansen solubility sphere method. For calculation of HSPs, the solubilities of asphaltenes were determined using a dynamic light scattering (DLS) method by dissolving or dispersing the asphaltenes in various solvents and measuring the particle size distributions thereof. The particle diameters of asphaltenes in good solvents were lower than its detection limit (<1 nm). It was demonstrated in the present study that asphaltenes differing in elemental composition had different HSP values corresponding to dispersion, dipole interaction, and hydrogen-bonding forces (δ d, δ p, and δ h, respectively). Experimental results suggested that the differences in HSP values of the asphaltenes were influenced by the H/C ratio, oxygen content, and average asphaltene molecular weight.

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)0.000
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations92
Published2014
Admission routes1
Has abstractyes

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