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Record W3118780466 · doi:10.5518/823

Data associated with 'Aggregation Behaviour of E-SARA Asphaltene Fractions Studied by Small-angle Neutron Scattering'

2020· dataset· en· W3118780466 on OpenAlexaff
Dewi A. Ballard, Peiqi Qiao, Beatrice Cattoz, Peter J. Dowding, Sylvain Prévost, Mohammed Alshamsi, Thibaut Charpentier, Kevin J. Roberts, Zhenghe Xu, David Harbottle

Bibliographic record

VenueUniversity of Leeds · 2020
Typedataset
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphalteneTolueneSmall-angle neutron scatteringPower lawChemistryFraction (chemistry)ExponentNeutron scatteringAnalytical Chemistry (journal)ScatteringChemical physicsChemical engineeringChromatographyOrganic chemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Using the Extended-SARA method to fractionate asphaltenes based on their interfacial activity, the current study reports the first results on the estimated size and shape of interfacially active (IAA) and remaining (RA) asphaltene nanoaggregates. These fractions have been reported to exhibit distinctly different chemical architectures that influence the size of asphaltene clusters in good and poor solvents. However, little is known about the building blocks, commonly referred to as nanoaggregates, which form these clusters and how those subtle differences in chemical architecture impact aggregation of asphaltenes. The nanoaggregate size and shape of IAA and RA was measured using small-angle neutron scattering (SANS). The characteristic length and asymptotic power-law exponent of whole asphaltenes (WA) extracted from heavy crude oil and dispersed in deuterated toluene were 28.0 Å 0.2 Å and 2.860.01, respectively, showing negligible variations with changing asphaltene concentration, source of asphaltenes (bitumen and heavy crude oil), and solvent aromaticity. For RA fractions, which account for 98.5 wt% of WA, the characteristic length and power-law exponent of 28.8 Å and 2.86 were comparable to that of WA, but in contrast to 59.7 Å and 2.20 for IAA. A ~100% increase in the characteristic length and reduced power-law exponent of the IAA fraction confirms that these two asphaltene sub-fractions form dissimilar nanoaggregate structures.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.007
Threshold uncertainty score0.960

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 science0.0010.000
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 head0.028
GPT teacher head0.231
Teacher spread0.204 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations2
Published2020
Admission routes1
Has abstractyes

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