Study on molecular structure and association behaviour of heavy subfractions of vacuum residue by an improved separation method
Bibliographic record
Abstract
Abstract Vacuum residue (VR) is the most complex component of crude oil. Due to the special structure of heavy subfractions, physical aggregation and chemical coking reactions easily occur through molecular force, which affects the normal processing. Therefore, in‐depth study and analysis of their composition, structure and association behaviours are particularly important. In view of the shortcomings of the traditional separation method in terms of separation accuracy, mainly including the purification of asphaltenes and the poor separation of resins. In this paper, a reasonably improved separation method is adopted, and the multi‐stage asphaltene extraction and the multi‐stage silica gel coupling separation are carried out innovatively, which achieves a high yield of 99% while ensuring the separation accuracy. The samples were characterized by elemental analysis (EA), gel permeation chromatography (GPC), Fourier‐transform infrared spectroscopy (FT‐IR), hydrogen nuclear magnetic resonance spectroscopy ( 1 H‐NMR), X‐ray photoelectron spectroscopy (XPS), X‐ray diffraction (XRD), and scanning electron microscope (SEM) to study their structural characteristics and association behaviours. The results show that the main forms of heteroatoms in asphaltene and resin surface are C‐O‐C, C‐OH, pyridine, pyrrole, and thiophene, and the content of these substances is higher in asphaltene. Compared with resins, asphaltenes contain a more peri‐condensed aromatic structure and shorter alkyl substituent side chains. By studying the hydrogen bond and acid–base interaction, it is found that asphaltene and resin mainly contain OH‐OH, OH‐π, and OH‐ether O, of which the content of OH‐OH is the highest. Asphaltene and resin have more neutral and basic substances. These hydrogen bonds and acid–base interactions caused by heteroatoms are the main forces for the association of the heavy subfractions of the VR.
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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 | 0.001 | 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) | 0.001 | 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".