Asphaltene Subfractions Responsible for Stabilizing Water-in-Crude Oil Emulsions. Part 2: Molecular Representations and Molecular Dynamics Simulations
Bibliographic record
Abstract
After successful isolation of the most interfacially active subfraction of asphaltenes (IAAs) reported in the first part of this series of publications, comprehensive chemical analyses including ES-MS, elemental analysis, Fourier transform infrared (FTIR) spectroscopy, and nuclear magnetic resonance (NMR) spectrometry were used to determine how the molecular fingerprint features of IAAs are different from those of the remaining asphaltenes (RAs). Compared with the RAs, the IAA molecules were shown to have higher molecular weight and higher contents of heteroatoms (e.g., three times higher oxygen content). The analysis on the elemental content and FTIR spectroscopy suggested that IAAs contained higher contents of high-polarity sulfoxide groups than the RAs. The results of ES-MS, NMR, FTIR, and elemental analyses were used to construct average molecular representations of IAA and RA molecules. These structures were used in molecular dynamics (MD) simulation to study interfacial and aggregation behaviors of the proposed molecules. The MD simulation study showed little affinity of representative RA molecules to the oil/water interface, while the representative IAA molecules had much higher interfacial activity, reflecting the extraction method. The aggregation of IAA molecules in the bulk oil phase and their adsorption at oil/water interface were not directly related to the ring system, but rather to the associations between or including sulfoxide groups. During the simulation, the IAA molecules were found to be self-assembled in solvent, forming supramolecular structures and a porous network at the oil/water interface, as suggested in our previous work. The results obtained in this study provide a better understanding of the role of asphaltenes in stabilizing petroleum emulsions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".