Aggregation Behavior of E-SARA Asphaltene Fractions Studied by Small-Angle Neutron Scattering
Bibliographic record
Abstract
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 asphaltene (IAA) and remaining asphaltene (RA) 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 (WAs) extracted from heavy crude oil and dispersed in deuterated toluene were 28.0 ± 0.2 Å and 2.86 ± 0.01, 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 subfractions form dissimilar nanoaggregate structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".